ML20137Z554

Evacuation Plan Update for Seabrook Station, Progress Rept 1

ML20137Z554
Person / Time
Site:	Seabrook
Issue date:	11/11/1985
From:	KLD ASSOCIATES, INC.
To:
Shared Package
ML20137Z542	List: ML20137Z540 ML20137Z554 ML20137Z582 ML20137Z611 ML20137Z615 ML20137Z629
References
TR-174, NUDOCS 8603130068
Download: ML20137Z554 (127)

Text

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TR~174

$f EVACUATION PLAN UPDATE for Seabrook Station Seabrook, New Hampshire Procress Report No. 1 Prepared for s The Commonwealth of Hassachusetts

( ) Civil Defense Agency and Office of E=ergency Preparedness by KLD Associates, Inc.

300 Broadway Huntington Station, NY 11746 November 11, 1985 s

' 0603130068 860311 PDR ADOCK05000gj3 F

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TABLE OF CONTENTS i

Pace INTRODUCTION 1 SCOPE OF FIRST PROGRESS REPORT 2 BACKGROUND 5 Initial Meeting: Defining the Scope of Work 6 Literature Review 6 Telephone Survey 7 On-Site Interviews 7 Developing the Evacuation Plan 8 Current Status and Projected Activities of the Work Effort 9 DEMAND ESTIMATION 11

' Trip Generation 12 Permanent Residents 13 Beach Population 15 Seasonal Housing Residents 20 Overnight Accommodations 23 Campgrounds 26 Seabrook Greyhound Park 26 Parking at Retail Establishments 26 Manufacturing and Industrial Employment 31 Seabrook Station 33 Medical-Related Facilities

'O Total Demand in Addition to Permanent Population 33 33 Uncertainties 33 ESTIMATION OF HIGHWAY CAPACITY 40 Capacity Estimations on Approaches to Intersections 41 Capacity Estimation Along Sections of Highway 43 General Considerations 45 Application to Seabrook EPZ 45 Two-Lane Rural Roads 46 Freeway Capacity , 48 Freeway Ramps 50 PRELIMINARY RESULTS 51 APPENDIX A - Glossary of Terms A-1 APPENDIX B - Traffic Assignment Model B-1 l -  ?

APPENDIX C - Traffic Simulation Model: I-DYNEV C-1 APPENDIX D - Detailed Description of Study Procedure D-1 APPENDIX E - Literature Review and Data Compiled to Date E-1 APPENDIX F - Telephone Survey Instrument F-1 l

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() TABLE OF CONTENTS (continued)

Page APPENDIX G - Tabulations of Telephone Survey Data G-1 APPENDIX H - 1980 Census Data H-1 LIST OF EXHIBITS Exhibit 1 - General Highway Map 3 Exhibit 2 - Estimation of Persons per Vehicle During Evacuation 16 Exhibit 3 - Comparisons of Beach Area Vehicle Capacities and Counts 21 Exhibit 4 - Fundamental Relationship between Volume and

(. Density 44

() LIST OF FIGURES E21 ' Title Page 1 Distributions of Elapsed Time for Various Pre-evacuation Activities 14 2 Household size Within Seabrook Station EPZ 17 3 Auto ownership of Households Within Seabrook Station EPZ 18 4 Weekend Vehicle Demand 22 5 Weekend Vehicle Demand - Inland 24 6 Rooms in Yearly and Seasonal overnight Accommodations 25 7 Rooms in Yearly and. Seasonal overnight Accommodations 27 (Estimates Exclude Vehicles at the Beach) j 8 Vehicles at Campgrounds 28 I

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' (-) (concluded) I LIST OF FIGURES E22 Title Page 9 Vehicles at Campgrounds 29 (Estimates Exclude Vehicle at the Beach) 10 Parking for Capacity (Vehicle Demand) on U.S. Highway 1 30 11 Parking for Capacity (Vehicle Demand) on U.S. Highway 1 32 (Estimates Based on 40 Percent Occupancy) 12 Manufacturing and Industrial Employment 34 13 Manufacturing and Industrial Employment 35

. (Estimates Appropriate During Peak Beach

. Conditions) 14 Capacity of Medical-Related Facilities 36 15 Estimated Vehicles at Medical-Related

() Facilities 37 16 Total Estimated Number of Evacuating Vehicles NOT at the Beaches and NOT Belonging to EPZ Residents 38 17 Elapsed Times from Start of Evacuation for Regions within the Seabrook Station EPZ 52 LIST OF TABLES ,

ug. Title PAqn 1 Estimates Vehicle Population - Permanent Residents 19

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1. INTRODUCTION This is the first of a series of Progress Reports which will dccument the activities performed, and the results obtained, in connection with a study to update the existing evacuation plan for the Seabrook Station. ,

j The practice of publishing progress reports for distribution i departs, somewhat, from the conventional practice of completing the study before publishing a Final Report. We have chosen this approach for the following reasons: ,

1. It is hoped that the early publication of these reports would prove of value to other planning groups.

2. Hopefully, these reports'will also stimulate responses from all involved public agencies, citizen planning committees and town officials.

3. The timing of these progress report publications will enable us to review all such responses -- both constructive and critical -- prior to the completion of the study. Any responses which are received in a timely manner and judged to contribute to the accuracy and/or reliability of the final study results, will be incorporated into the evacuation plan development, prior to the publication of the Final Report.

() This approach is endorsed by the Massachusetts Civil Defense Agency (McDA), which is sponsoring this activity.

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2. SCOPE OF FIRST PROGRESS REPORT S This report describes the activities undertaken to date and presents results of the analysis of the first evacuation scenario considered. ,

It must be emphasized that these results are oreliminary in nature and will be refined in the near future. If we receive further information which substantively changes our estimate of traffic demand, or our representation of the highway system, then the results will change accordingly. Also, further refinement of Traffic Management strategies will tend to lower, somewhat, the ETE presented herein.

Reflecting the preliminary nature of these results, we have not included detailed maps of the highway system depicting the recommended routes. As we proceed in finalizing this work, such routes will certainly be delineated. The accompanying map displays the area of interest and shows all the major routes.

During the course of our travels throughout the Emergency Planning Zone (EPZ), we have discussed aspects of our work with many public employees, elected officials and members of the public. A frequent topic was the evacuation of the beach population. To respond to this interest, we are analyzing the following scenarios

1. Summer weekend, with sunny, hot weather O 2.

3.

Beach population at capacity Tourist population at capacity

4. Order to evacuate is issued at the time when beach population is at capacity, roughly at 2:00 PM.

We do not suggest that this scenario necessarily represents the condition which leads to the maximum value of Evacuation Time Estimates (ETE). Other scenarios, e.g. involving inclement weather conditions, may lead to longer values of ETE. The full study when completed, will have computed ETE for all relevant scenarios needed for the purpose of determining protective actions in the event of an emergency.

Again, we emphasize that these initial results are oreliminary. We have not as yet addressed the evacuation of transit-dependent persons, nor other elements of the evacuation plan. Rather, we have focused, no far, on the evacuation of the vast majority of the public from the EPZ, who will une private vehicles.

Experience has shown that the impact, on ETE, of transit vehicles which share the highways with these private vehicles, in very small. Of course, the ETE of transit vehicles, as a group, may differ significantly from that of private vehicles; thus the evacuation of transit-dependent p9rsons must be studied in O

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h detail, within the context of the overall evacuation. Separate O- ETE must be developed for different groups of transit-dependent persons, as required by NUREG 0654. This work will be addressed sno tly.

Appendices A through D are included to provide the interested rhater with additional details of the procedures employed to analyze emergency evacuation scenarios.

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-s 3. BACKGROUND

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This section reviews the activition of KLD personnel sinco the start of work which commenced on August 21, 1985. These activities included:

e Initial meeting with the Massachunotts Civil Defense Agency (MCDA).

e Review of existing reports doncribing past evacuation studien.

e Conducting a field survey of the EPZ highway system and of beach-area traffic conditions.

e Retaining a subcontractor to acquire data describing beach traffic in the Salisbury-Seabrook-Hampton area on the weekends of August 24th and 31st (Labor Day weekend) and the mid-wook period between these weekends.

e Developing a survey instrument to solicit data doncribing the travel patterns, car ownernhip and household size of the population withis the Seabrook EPZ. This survey also obtained data on the public's projected risponnen to an e=orgency at Soabrook Station.

e Retaining a subcontractor to conduct a otratified

/ random-sample survey of the populace within the Seabrook C] EPZ.

e Undertaking on-sito interviews with emergency planning personnel (Fire Depts., Polico Depts., State Troopers, Planning personnel, Public Works Dept., Town Managorn):

Town elected officialst Regional Planning agencient Chambers of Commerce; State Parks Dept., Highway and Planning officials; and Citizen Emergency Planning Committoon.

e Attending a meeting with FEMA personnel at Region 1 Headquartern.

e obtained demographic data from State Planning officon.

o Received, and analyzed, aerial photographn of the coastal arcan within the Seabrook EPZ.

e Developed link-nodo configuration of evacuation network, which in used as the basin for computer analysis of an Evacuation Plan and computation of ETE. Specifically, wo will uno the evacuation modal developed by KLD for FEMA, which in the IDYNEV System.

e Prepared the input stream for the IDYNEV syntom for subsequent procanning.

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i O e Executed IDYNEV to provide the initial estimates of evacuation routing and ETE for the single scenario considered to date.

The following section describes these activities in detail.

Then the current status of the work effort is described, followed by a projection of activities to follow.

Initial Meetinct Definina the Scoce of Work I The initial activity was a meeting with Mr. Robert Boulay, Director of the Massachusetts Civil Defense Agency (McDA). At s that meeting, Mr. Boulay outlined the scope of our activities:  ;

e To update the current evacuation plan and to compute revised Evacuation Time Estimates (ETE).

e To acquire whatever current information is needed for this activity.

e To meet with the six EPZ Planning Committees to solicit information, to describe the activities which are being undertaken and to address s'ny concerns which are expressed.

l e To cooperate with all other emergency planning groups and  ;

public officials and emergency personnel, both in O Massachusetts and New Hampshire. ,

r e To report all progress to, and accept direction from, Mr. i Buzz Hausner, MCDA consultant.

Literature Review i

XLD Associates was provided with copies of documents describing past studies and analyses leading to the development -

of evacuation plans and of ETE. We also obtained supporting documents from a variety of sources, which contained information needed to form the data base used for conducting evacuation ,

analyses. t Appendix E is a listing of the major sources of information and includes brief descriptions and summaries of the data contained therein. -

Field Surveys i

XLD professional personnel drove the entire highway system I within the EPZ and for some distance outside. Each driver i recorded the characteristics of each section of highway on audio tape. These characteristics includes O

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i O Number of lanes Pavement width Posted speed Actual free speed shoulder type & width Abutting land use Intersection configuration Control devices Lane channelization Interchange geometries Unusual characteristics: Geometries: curves, grades Narrow bridges, sharp curves, poor pavement, flood warning signs, inadequate delineations, etc.

The audio cassettes were then transcribed; this information was referenced while preparing the input stream for the IDYNEV model. '

Field surveys were performed both during weekdays and on weekends. Much of the time on weekends was spent at the beach areas. Unfortunately, congested conditions were limited along the beach access roads for both weekends due to mild -- but not hot -- weather, plus occasional rain. One Saturday night, however, the weather was pleasant and the beach areas were crowded.

Telechene survov i

A telephone survey was undertaken in order to gather information needed for the evacuation study. Appendix F exhibits the survey instrument. Appendix G contains tabulations of nomo .

of the data compiled from the survey returns. We are continuing  !

(]) to process this data.

This data was utilized to develop estimaten of vehicle occupancy during an evacuation and to estimate elapsed times between the issuance of an evacuation order and the start of evacuation trips.

On-Site Interviews KLD personnel visited the EPZ area on a bi-weekly basis during the first 2 1/2 months of this project. Each visit consisted of from 2 to 4 days; each day included neveral interviews with different groups of people. {

These interviews consisted primarily of KLD pornonnel acquiring information which could prove unoful for developing an evacuation plan, participants in thana interviewn included town l police and fire chiefs, emergency planners, public work l supe rvisors, town managern, elected officials, chamber of commerce personnel, State planning and highway personnel, regional planning commission personnel, Stato parks personnel, t and State emergency planning personnel. In addition, KLD was invited to address two citizen emergency planning committoon. At these meetings, the KLD representativo described the work offort and responded to all quantions. Visits were also mado to thu  ;

scabrook Station to gather information.

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Develonina the Evacuation Plan The overall study procedure is outlined in Appendix D. 1 particular attention was focused on estimating tourist traffic, '

especially that which is concentrated in the beach areas. Aerial photographs were obtained which were used to estimate parking

capacity at the beach areas and counts of vehicles parked at the beach areas. Other photographs enabled us to estimate maximum ,

people density on the beach itself.

Demographic data was obtained from several sources, as  !

detailed later in this report. This data was analyzed and  ;

i converted into vehicle demand data.

Highway capacity was estimated for each highway segment based I on the field surveys and on the principles specified in the 1985 ,

Highway Capacity Manual (HCM). The link-node representation of '

the physical highway network was developed using large-scale maps and the observations obtained from the field survey. ,

, The input stream for the IDYNEV system was then created, ,

i checked, and debugged. Appendices 8 and C briefly describe the l major component models of this system.

i The evacuation analysis procedures are described in Appendix

{ D. First, a Trip Table

• is designed, based upon the need tot O e aoute traffic atons raths of eravet that witt i

! expedite their travel from their respective points of ,

origin to points outside the Ep2  ;

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restrict movement toward Seabrook Station to the extent ,

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disperse traffic demand so as to avoid focusing demand j on a limited number of highways I e Satisfy, to the extent possible under emergency conditions, perceived "best" paths out of the Ep2  ;

e Move traffic in directions which are generally radial, '

j relative to the location of Seabrook Station.

• With the Trip Table specified, the IDYNEV Traffic Ansigrment ,

model is executed. This model produces output which identifien  ;

j the "best" traffic routing, subject to the design conditions 1 1 outlined above. In addition to this information, (very) rough  !

estimates of travel time are provided, together with turn-movement data required by the IDYNEV simulation model.

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• A matrix of origin-destination demand volumes.

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f3 The simulation model is then executed to provide a detailed i ) description of traffic operations on the evacuation network.

This description enables the analyst to identify bottlenecks and to develop countermeasures which are designed to expedito the movement of vehicles.

As outlined in Appendix D, this procedure consists of an iterative design-analysis-redesign sequence of activities. If properly done, this procedure converges to yield an Evacuation Plan which best services the evacuating public. At the present timo, we are approximately midway through this process for the scenario under consideration.

Current Status and Proiected Activities of the Work EffpIt We are nearing the completion of the data acquisition phano of activity subject, of courne, to any information submitted by roadors of this report. At this timo, we are entering the analysis and design phase of the activity, as doncribed in Appendix D.

In the immediate future, we will

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e Refine the data and check its accuracy e Examino any now information providad, and rovino the existing data base, as appropriate e Develop the traffic management strategies to expedito the movement of vehicles -- and people -- from within the Seabrook EPZ e Expand the number of scenarion to satisfy the NUREG 0654 Federal guidelinos e Analyze all scenaries -- check the traffic management strategios for each - and obtain the ETE e Develop the plan for transit-dependent ovacusan e continuo to communicate with local planning groupn.

Traffic management strategion will bo developed next to reflect the routing which is identified by the IDYNEV model.

Work will continue, in parallol, to analyze other ovacuation aconarion.

The scenarion to be considered next includet e Evacuation of antiro EPZ on a nummer wookond in the prononce of incicmont weather 9

O e Evacuation of entire EPZ on a suaner weekday under good and inclement weather conditions.

The other scenarios will follow.

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4. DEMAND ESTIMATION

() The estimates of demand constitute a critical element in developing an evacuation plan. This estimato consists of three components:

1. An estimate of populatica, stratified appropriately, and related to the locati:ns within the EP .

2. An estimate of mean occupancy per evacuating vehicle, to determine the number of evacuating vehicles.

3. An estimate of potential double-counting of vehicles.

A variation of this approach was applied in order to estimate beach area traffic. This was neconsary since the majority of beach traffic consists of transients, many of whom enter the EP:

from locations outside.

As a result, we relied on empirical observation of the number of vehicles which can physically be accommodated within the bosch area. This is a val:1 approach since discussions with public officials confirmed that, with few exceptions, peopio at the beach have access to a vehicle.* Thus, the evacuation of people from the beach area will be primarily reflected in the number of ovacuating private vehicles.

i By accurately estimating the number of vehicles on the beach I

(]) area, we have satisfied the input requirements for an evacuation plan. Estimates of population can be based on accurate estimates of per-vehicle person occupancy. Thus, for the beach area, more rollable estimates are forthcoming if vo reverne the sequence of stops 1 and 2, above.

During the summer season, vacationers and tourints ontor the EPZ in large numbers. These non-residents may dwell within the EPZ for the entire season, for a short period (e.g. one or two wooks), for a weekend, overnight, or may enter and leavo within one day. Estimates of the size of each of those population components must be obtained, so that the associated number of vehicles can be ascertained.

• The NRC report authored by Kaltman estimated the number of persons who may have arrived at the beach by hitch-hiking, via transit vehicles, or being " dropped off". Holative to the I total, these exceptions constituto about two percent of tro i total population at the beach area. Thono relatively few exceptions can have accenn to carn driven by others (i.e.

1 ride-sharing). In addition, the evacuation plan will provida

, trannit vehiclon for thone who are not able to ride-share.

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The spectre of double-counting of people and vehicles must be O addressed: a vehicle and its occupants cannot occupy two disparate locations at the same time. Consider a vacationing family that registers at a motel, travels to the beach in the morning, then does some shopping, away from the beach, in the evening before returning to the motel. If we consider a scenario where the accident occurs at about 2:00 PM when the beaches are most crowded, then this family, and its vehicle, are at the beach. If an evening scenario is being studied, then the vehicle is at a retail parking lot, or perhaps, back at the motel.

Clearly, since this vehicle cannot be at all 3 locations simultaneously, its location at the instant an advisory to evacuate is announced depends on the scenario being studied.

It is seen that the number of vehicles at each location depends on time of day. It is clearly VIgnq to estimate counts of vehicles by simply adding up the capacities of different types of parking facilities, without considering the whereabouts of the vehicles. For example, motel parking lots which are full at dawn, may be almost empty at noon. Similarly, beach parking lots which are full at noon, may be almost empty at dawn.

Another element that must be considered in an evacuation plan is the need to provide for transit-dependenc people. These people may be youngsters in school, persons in institutions without access to private vehicles or who cannot provide for C themselves, as well as residents and tourists who do not have access to a private vehicle. We do not consider this element in this first study, but will address it in subsequent work.

The following sections present the methodology for estimating vehiculur demand throughout the EP2, for the single scenario of galh, weekend, summer nid-day conditions. It is assumed that the parking capacity at the beach areas is fully exploited, that the weather is fair and that police and other smargency workers have been mobilized prior to the onset of major congestien associated with the evacuation.

Trio Generation Evacuation trips do not "just happen". These trips are

" generated" at the time the vehicle leaves its " origin" (i.e.

driveway of a residence, motel lot, public parking lot, etc.) to j begin the evacuation trip.

Between the time the evacuation is ordered, and the time that the evacuation trip begins, the evacuees may be performing a sequence of preliminary activities, depending on time-of-day and other scenario considerationst j e Commuters will prepare to leave work and secure their places of business, if necessary.

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, -) e conmutors will travel home from work.

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e Families will pack clothen and other provisions, and secure their homon (or farma).

Another timo lag in notification timo -- the clapsed timo between the issuance of the order to evacuato, and the receipt of this notico by members of the public.

Thoso elapsed timon will vary from one scenario to the next and, of courso, from one hounohold to the next. Thus, the trip generation time (i.e. the elapsed time betwoon tho innuance of the order to evacuato and the beginning of the evacuation trip) will vary from one group of people in a vehicle, to another.

We can stato that the timo lag annociated with each preliminary activity can be represented by a gratistical distribution which doncribos the range of elapsed timon for the evacuating public. The survey (neo Appendix F) obtained information which quantified 3 of those distributional Figure 1 displays thoso distributions.

For each scenario, we must perform a series of calculations, using the distributions of Figure 1, plus a reasonable estimate of the distribution of notification timo, to obtain the distribution of Trip Gonoration time.

Experience -- and theory -- indicate that ETE in generally Os inconsitivo to this distribution of Trip Conoration timo, whenover the temporal extent of the trip generation procoon in significantly lens than the evacuation time (ETE).

At thin time, we have not as yet computed the trip gonoration distribution. (This will be dono in the near torm.) Daned on a review of the relevant data, wo estimato that the trip generation will extend over an interval of about 3 hourn. We have used an approximato trip generation for this initial analysin. No substantivo difference in ETE is anticipated when the refined, calculated, distribution in unod, for the reason given above.

P3rnnont PqnidanM The ostimaton of permanent population within the EPZ are given in Appendix E, Item 15. The two major noureco of those data -- Stato projections and Town Clark ostimaton -- are in general overall agrooment, but with some important differences at the town level.

Wo have decided to accept the Town Clark'n estimaton sinco they represent data acquired in early 1905 and may thoroforo be more accurato.

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0 l. 0.5 1.0 1.5 2.0 2.5 3 '. 0 3.5 4.0 Elapsed Tice, I!ours Figure 1. -Distributions of Elapsed Tire for Varicus Pre-evacuation Activities

The second step of the estimation process in presented in g3 Exhibit 2. Supporting data are presented in Figures 2 and 3 and

( ,) in Appendix G.

Using an average vehicle occupancy of 2.6, the number of evacuating vehicles may be calculated. Table 1 prosents those results.

Beach Population In general, it is necessary to partition the beach area population intos e Permanent residents e Seasonal residents e overnight residents e Transients (i.e. day-trippors).

To estimato the number of people and vehicles during gnah conditions, such partitioning is largoly academic. In a practical sense, beach traffic is generally limited by parking capacity, according to discunnions we have hold with public officials.

The available parking in the beach arean tako neveral forms o Parking lots, both public and privato e Parking arean reserved for guests, e.g. hotels, motoln, f')

'- e cottagon, condominiums Curb parking e Drivoways, backyards, front yards, other acconsible, unattended aross.

On several beachen, curb parking in available only to those who exhibit permits. Novartholoon, we annumed that all curb space, except whero they block drivoways, could be fully utilized.

Appendix E, Item 7, prosents a list of estimated parking capacity at the beach arcan, and a list of vehiclo counts on the most crowded wookond day that was recorded on aerial photographa in our ponnonnion. The capacity totaled 25,470 apacon while the number of vehicles counted totaled 10,220 or about 72 percent of estimated capacity.

Our studian indicato that beach population can vary widely, from day to day, depending most strongly on weather conditions.

Doach population also varion with timo of day. On a nunny day it generally peaks at about 2 PH; another, lower, peak occurs at night. It therefore apponra nonsible to conduct a norion of nonsitivity studien to datormino the "clasticity" of ETE with ranpoct to beach population. This study in planned for the n9ar te rm .

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{} EXHIBIT 2 Estimation of Persons eer Vehicle Durine Evacuation

1. Assume 1

(a) All households with 4 or fewer persons will ride in one car, if they have a car availablo.

(b) All households with 5 or more persons and with 2 or more cars, will ride in 2 cars. The remainder will ride in one car, if available.

H.H. Size Eo. of H.H. No. of Cars Used Egrsons/ Car 1 187 0.87 x 187 = 163 1 2 450 0.98 x 450 = 441 2 l 3 246 246 3 l

4 247 247 4 5 106 (2x0.86+0.14)l06 = 190 2.79 6+ 64 (2x0.78+0.22)64 = 114 4.04*

TOTALS: 1300 1401 2.68 avg.

2. Assumes (a) All households with 3 or fewer persons will ride in one O o r if ev 11 ate-(b) Italt of all households with 4 persons and two or more cars will take two cars; the others will take 1 car.

(c) All households with 5 or more persons with 2 or more cars will take 2 cars. The remainder will take one car.

11 . 11 . Size No. o f 11.11. No. of Carn Ungd Personp4 Car 1 187 0.87 x 187 = 163 1 2 450 0.98 x 450 = 441 2 3 246 246 3 i

4 247 (1/2x0.81x2+0.595)247 = 347 2.95

'l 5 106 (2x0.86+0.14)106 = 190 2.79

, 6+ 64 (2x0.78+0.22)64 = 114 4.04*

l TOTALSI 1300 1501 2.51 avg.

Conclusionn For our initial estimates of ETE we will employ an estimate of 22A persons por car, as an average.

a . . . based on an avg. 11.11. sizo of 7.2 pornons.

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Figuro 2 Household Size Within Seabrook Station EPZ l

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e (22) (23)

" (14)

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Cars AvaLlable Carn Available rive-Person Ifouseholds Six Plus-Person flouncholds 18

_ . - - - - . -- - _ _ - --- _- . - -~ -_- - ._ - -.-. .. -

i

/I Table 1. Estimated Vehicle Population - Permanent Residents Proj. Est.

Est. Population Growth Pop. Vehicles Massachusetts Town clerks 1985 Rate (cct) (1986)' (1986) ,

Amesbury 14,056 1.44 14,258 5,484 Merrimac 4,364 1.28 4,420 1,700 Newbury 5,423 1.04 5,479 2,107  ;

Newburgport 16,300 0.70 16,414 6,313 Salisbury 6,645 1.23 6,726 2,587 West Newbury 3,260 1.10 3,296 1,268 I New Hameshire Brentwood 2,000 1.94 2,039 784 E. Kingston 1,250 0.96 1,262 485 Exeter 11,600 1.50 11,744 4,528 t

Greenland . 2,200 1.15 2,225 856 l Hampton i 13,000 1.80 13,234 5,090  !

Hampton Falls 1,450 1.65 1,474 567 Kensington 1,350 2.57 1,385 533 Kingston 4,890 3.93 5,085 1,955

. New Castle 625 -0.62 621 239 Newfields 850 2.11 868^ 334 Newton 3,625 3.27 3,744 1,440 '

North Hampton 3,600 1.05 3,638 1,399 Portsmouth 26,300 2.21 26,881 10,339  ;

Rye 5,000 1.98 5,099 1,961 Seabrook 8,000 1.97 8,158 3,138 l South Hampton 700 -0.19 699 269

, Stratham _ 3.300 4.39 3.445 1,325 Totalst 139,788 (Avg.) 1.72 142,194 54,701

) Note Compounded annual rates were calculated using State data

for the years 1980 and 1985.

I l

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j 19

3 -.-

It is instructive to compare the estimates of beach area vehicle population presented by other investigators, with those O obtained by KLD using the aerial photographs. Exhibit 3 lists these comparisons. As is indicated, the KLD estimates of capacity exceed other estimates of vehicle counts and of capacity, by a reasonable margin.

Seasonal Housina Residents i' The vehicle population on the beach areas, which rep esents weekend occupancy of seasonal housing there, has already been included in our count of beach parking. However, we must consider the vehicle population which is gff the beach areas, and which services seasonal residents.

A primary source of double-counting must be considered. If the beaches are packed to capacity, it follows that at least a portion of the vehicles servicing seasonal vacationers located i

off the beach, will have been driven to the beach and parked there. of course, some of the seasonal dwellings are close enough to the beach for people to leave their cars and walk to the beach. To the extent that vehicles are driven to the beach, it would be improper to count these vehicles at the seasonal dwelling and at the; beach.

In order to estimate tourist population, not included in the beach area vehicle count, we seek an estimate of the number of those vehicles which remain in the EPZ at the time the beach is Q most crowded, but are not at the beach.

We will, therefore, accept the figures provided by the NRC*,

as shown in Figure 4, with the following exceptions:

1. We will exclude from consideration those vehicles at seasonal housing which are located on the beaches, since these vehicles have already been counted. Excluding the vehicles in the seaward sectors of Figure 4 from 2 miles, outward, will avoid double-counting of the beach vehicles.

2. Based on discussions with managers of tourist facilities, they estimate that 74 percent of visitors of off-beach facilities will travel to the beach and park their vehicles there, during mid-day peak weekend consitions.

We will adopt a conservative factor of 50 percent (i.e.

one-half of tourists lodging at inland facilities will drive to the beach on a sunny day).

• see the NRC report prepared by M. Kaltman and referenced in Item 14, Appendix Et also see related text of Item 14.

20

EXHIBIT 3 r^N t

~

Comparisons of Beach Area Vehicle Canacities and Counts (Refer to Appendix E for details)

1. Dufresne-Henry Report (Item 3, App. E):

o Capacity of all parking areas in Hs=pton Beach, including on-street:

4,034 cars co= pared with e

KLD estimate of parking capacity, including driveways, backyards, etc.: 7,770 cars

2. The SNHRPC Report (Item 6, App. E):

o Count of parked cars on a crowded weekend, on N.H. beaches: 12,650 cars e KLDestimatsofparkingcahacityinN.H.: 14,580 cars

3. The EMM Report (Item 13, App. E):

(~)

e Daily transient (i.e. seasonal, overnight, daily) vehicles on weekend: 17,147 cars e KLD estimated capacity: 25,470 cars

4. The NRC Reports (Item 14, App. E):

e Estimates of beach area parking during peak conditions, within the EPZ: 19,700 cars e KLD estimated capacity: 25,470 cars

5. The Costello, et al., Report (Item 16, App. E):

This report does not break out the estimate of beach area traffic, explicitly, so no comparison is possible.

21

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Note that the NRC estimates assume 2.5 vehicles per dwelling, a figure which we confirmed with an on-site survey. The results are shown in Figure'5.

Overnicht Accommodations Again, the vehicles associated with those hotels, motels and guest houses located on the beach area have already been counted.

We must also consider vehicles associated with such accommodations which are located off the beach.

'The need to avoid double-counting for these vehicles leads to consideration of the following:

~

e Many patrons of overnight accommodations do not arrive at the facility until late afternoon or early evening, after the beach population has dropped from its. peak content, e Many patrons also leave the area in the early morning before peak conditions occur on the beaches.

e other patrons stay for several days. Of these:

Some depart the facility to go to the beach or me- -

other attraction.

The remainder stay at the facility to swim in the pool-or walk in the area.

e The number of cars per unit for off-beach motel / hotel

. accommodations may'be less than one because:

A family, or friends, travelling in one car may occupy two units.

Travelers on one charter bus may occupy many units.

In any event, we seek an estimate of the number of'these vehicles which are within the EPZ at.the time the beach is most crowded, but D2t at the beach.

i We will accept, as a basis, the NRC count of overnight

accommodation units, as shown in Figure 6. Based on discussions i cited earlier we estimate that about 50 percent of the vehicles

! servicing these units remain within the EPZ, but not at the beach j when peak conditions prevail there. These disussions also

, revealed that several of the largest hotels set aside blocks of rooms on the weekend for guests who arrive by tour bus, at the rate of about 20 units for one tour bus.

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Estimates of guests who utilize more than one unit per car, O. range from 5 percent to 40 percent. On the basis of this information, we estimate that approximately 0.85 vehicle per unit,' is a reasonable expectation.

The results of an analysis to estimate off-beach vehicles at mid-day who remain within the EPZ are shown in Figure 7.

Camecrounds The estimate of the number of vehicles at campgrounds within the EPZ is developed in a manner which is similar to that for seasonal residents. The primary difference is that 2.5 vehicles are assumed for each seasonal dwelling while campground. capacity is expressed in terms of sites, where one vehicle per site is standard.

We again accept the NRC data, as shown in Figure 8, excluding, as before, the vehicle spaces which were already included in our beach area count. In addressing the issue of double-counting, the considerations are essentially the same as noted earlier for the overnight accommodations.

. . Based on discussions, campground operators estimate that approximately 75 percent of campground sites are unoccupied by a vehicle during the day when the beach exhibits. peak occupancy.

Most of these vehicles are driven to the beach areas, with the m remainder leaving the area. As a result, the number of additional. vehicles, not at the beach, but remaining in the area and must be considered, is shown in Figure 9.

Seabrook Greyhound Park This park, which is located a little over 2 miles west of the Station, has a parking lot with capacity for about 3,100 vehicles, according to the NRC report. We seek an estimate of the maximum number of parked vehicles during the weekend mid-day, when the beach is experiencing peak attendance. Those vehicles belonging to permanent residents must be excludea since they are counted elsewhere. Based on the information available, we have estimated a figure of 1,500 vehicles at mid-day. We have

. requested more detailed information and will revise this estimate when it arrives.

Parkina at Retail Establishments The NRC report presents an estimate of vehicles parked in lots servicing retail. establishments, e.g. shopping centers, restaurants, large stores, municipal lots, etc. This estimate, shown in Figure 10, is premised on the assumption of 100 percent occupancy. The applicant indicated that some 40 percent of the spaces are filled at maximum periods during the summer.

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Several factors should be considered:

e Do the " maximum periods" occur concurrently with the' peak periods of beach attendance?

e What percentage of parked vehicles belong to permanent residents?

We have not been able to acquire data to respond to these questions; therefore, we will make no deductions to account for the possibility that there are different peak periods for shopping and for beach traffic. On the other hand, it is not reasonable to assume that all lots servicing retail establishments are filled to capacity on a day when the weather attracts people to the beach area.

Adoption of the estimate of 40 percent occupancy appears to be prudent, in the absence of other empirical evidence. This estimate is shown in Figure 11.

Manufacturina and Industrial Emeleoment The NRC report presents an estimate of employment and issumes that each person drives his/her car to work. Our survey indicates that some 55 percent of commuters residing within the EPZ also work within the EPZ. Also, the 1980 census reveals that vehicle occupancy on the Home-based Work trip is approximately

(]) 1.16.

The NRC argues that employment should not be considered for the peak weekend scenario since workers would not be at their jobs. During our interviews with Town officials, we were told that beach attendance was " weather-driven". That is, a hot, sunny mid-week day, particularly on Friday, would attract a large number of people to the beach such that the crowds could be comparable to those on a weekend.

It is therefore prudent to consider the possibility of peak attendance at the beach areas on a Friday, or other weekday, when (most) commuters would be at work. From another point of view, even if manufacturing and industrial employment is less on a peak weekday, it follows that service employment will increase. Thus, we will apply the following estimate for the number of vehicles servicing employees:

1. We will accept the NRC estimate for employment.

2. We seek that component of total e=ployment, who live outside the EPZ. (The vehicles of residents are counted elsewhere.) Thus, we multiply these employment figures by 45 percent.

31

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3. These employment estimates are then divided by 1.16 to obtain the vehicle estimates. Thus, the relevant number

'( ') of vehicles at employer parking lots is 40 percent of the number of employees.

The resulting estimates are shown in Figure 13.

Seabrook Station Employment at Seabrook Station will probably stabilize in the area of 300-400 after construction is completed. On this basis, we estimate 500 vehicles there, to account for any contractor and visitor vehicles, in addition to commuter vehicles.

Medical-Related Facilities The NRC report presents estimates of the population of facilities such as hospitals, nursing and retirement homes and other health-related facilitiest see Figure 14. The number of vehicles associated with this estimate depends on the patients' state of health. Buses can transport up to 40 peopler vans, up to 12 people; ambulances, up to 2 people (patients).

Subject to subsequent verification, we will assume an average vehicle occupancy of 6 persons per car-equivalent at this time.

The resulting vehicle demand is shown in Figure 15.

Total Demand in Addition to Permanent Poculation

() The total number of vehicles, which are off the beach and are in addition to those servicing permanent residents, is obtained by summing the entries in Figures 5, 7, 9, 11, 13, and 15, then adding those at Seabrook Park and at Seabrook Station.

These totals are shown in Figure 16.

Uncertainties Every plan which forecasts events which can take place, definitionally involves some uncertainties. Such uncertainties, do not compromise the effectiveness of a plan if they are accounted for in a reasonable manner.

The statistics derived by the NRC and cited in the prior subsections are the outcome of a painstaking effort by HMM Associates. These results were reviewed and refined by the NRC.

Finally, additional data obtained by KLD addressed the issue of double-counting which did not receive attention previously.

The NRC data did not extend beyond the 10-mile radius to the EPZ boundary. KLD has contacted the Chamber of Commerce in Portsmouth. They have kindly offered to send a list of tourist facilities in that area since they have no data on tourist capacity. We will call these facilities after we receive that

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,a_ w t 'F z \

c 3 n i - r a

w s

'c 3s c

i O

1 FIGURE 16. Total Estirnated flumber of Evacuating Vehicles NOT at the Beachos and fiOT Delonging to EPZ Hesidents 38

- _ _ _ _ _ - - - - - - - - ~ ~ ' ~ ~ ~ ~ ~ ~ ~

list. Until then, we now estimate 1,000 additional tourist T vehicles are stored there outside the 10-mile boundary, but

{s' s within the~EPZ.

The towns of Brentwood, Greenland, Kingston and Newfields also have large areas within the EPZ but outside the 10-mile boundary. The first 3 have ca=pgrounds but no hotels; we obtained the necessary information from the respective town governments. We have not gotten through to Newfields as yet.

There will be vehicles travelling through the EPZ (external-external trips) at the time of the accident. It is reasonable to expect that, at the time evacuation gets under way, these through travellers will also be evacuating since they are already in their cars. We estimate about 2,000 of these additional vehicles; this figure will be reviewed by examining State traffic count data.

There may be some additional vehicles we have overlooked. In recognition of this possibility, we have made no adjustment for double-counting residents who are at the beach. That is, adding beach area vehicles to permanent resident vehicles, we have double counted those permanent residents who are at the beach when the evacuation order is given. This deliberate omission is to co=pensate for any vehicles we have overlooked in the demand estimation effort.

O O

39

/^^ 5. ESTIMATION OF HIGHWAY CAPACITY

()

The ability of the road network to accommodato demand is a major factor in determining how rapidly an evacuation can be completed. It is, therefore, necessary to know the capacity of the available roadways.

In general, the capacity of a facility is defined as the maximum hourly rate at which persons or vehicles can reasonably be expected to traverse a point or uniform section of a lane or roadway during a given time period under prevailing roadway, traffic and control conditions. (From the 1985 Highway Capacity Manual.)

In discussing capacity, different operating conditions have been assigned alphabetical designations, A through F, to generally reflect varying traffic operational characteristics.

These designations have been termed " Levels of Service". For example, Level A connotes free-flow and high-speed operating conditions; Level F represents a forced flow condition. Level E describes traffic operating at capacity.

Because of the effect of weather on the chpacity of a roadway, it is necessary to adjust capacity figures to represent estimated road conditions during inclement weather. Based on limited empirical data, weather conditions such as heavy rain gs reduce the values of capacity for highways by approximately 17

(_) percent. For inclement weather conditiens during the winter months, we have estimated capacity reductions of approximately 30 percent relative to normal weather conditions. We also reduce free flow speeds for inclement weather conditions: 20 percent for rain, 30 percent for winter.

In the congasted traffic environment which is often characteristic of an evacuation scenario, travel ti=e on a roadway section is, to a large extent, determined by the capacity Of that section. For that reason, estimates of roadway capacity must be determined with great care. Because of its importance, a brief discussion of the major factors which influence capacity, is presented in this section.

The major factors which control capacity include:

e On the approach to intersections

- Saturation queue discharge headways

- Turning movements

- Competing traffic streams

- Control policy.

e Along sections of roadway

- Roadway geometrics Traffic composition

(_) -

-4 3

[ um .

(s) e General considerations Weather conditions Pavement conditions Lighting Cacacity Estimations on Accroaches to Intersections At-grade intersections are apt to become the first bottleneck locations under heavy traffic volume conditions. This characteristic reflects the need to allocate access time to the respective competing traffic streams by exerting some form of control. During evacuation, however, control at critical intersections, will often be provided by traffic control personnel assigned for that purpose, whose directions may supercede traffic control devices.

The capacity of an approach to an intersection can be expressed in the following form:

3600 (G-L) 3600 T Qcap,m " h C h m

~

m . . m where (q_~) Qcap,m = capacity of traffic on an approach, which executen movement, m, upon entering the intersection; vehicles por hour (vph) hm = Mean queue discharge headway of vehicles on an approach, which are executing movement, m; seconds per vehicle Gm = The mean duration of GREEN timo servicing vehicles on an approach, which are executing movement, m, for each control cycle; seconds L = The mean " lost time" for each control cycle; seconds C = The mean duration of each control cycle; seconds Pm = The proportion of time allocated for vehicles executing movement, m, from an approach. This value is specified as part of the control treatment.

m = The movement executed by vehicles after they enter the intersection: through, left-turn, right-turn, diagonal.

The turn-movement-specific mean discharge headway h m, depends in a complex way upon many factors: roadway geometrics, turn percentages, the extent of conflicting traffic streams, the

(])

41

control treatment, and others. A primary factor is the value of

~N " saturation queue discharge headway", hsat, which applies to k' J through vehicles which are not impeded by other conflicting traffic streams. This value, itself, depends upon many factors including motorist behavior, but is relatively straightforward to determine empirically *in the field. Formally, we can write, hm"fm (hsat, F,1 Fr 2 ***)

where hsat = Saturation dischargu headway for through vehiclest seconds per vehicle F,1 F2 = The various known factors influencing hm fm (*) = Complex function relating h m to the known (or estimated) values of hsat, F, 1 F2 The estimation of h m for specified values of hsat, F, 1 F,

2 ... is undertaken by a mathematical model* which has been programmed into the Traffic Assign =ent and Traffic Simulation software of the IDYNEV System. The resulting values for h m -

always satisfy the condition: i ,

hm 1 hsat

-s That is, the turn-movement-specific discharge headways are always

(_) more than, or equal to, the saturation discharge headway for through vehicles.

It is seen that, given the ability to determine hm from hsate the determination of capacity of the approaches to intersections depends upon obtaining estimates of hsat. Such estimates were obtained empirically at representative intersections throughout the EPZ. In all cases, the values of hsat used in developing the evacuation plan represent conservative estimates ** based on this empirical data. Specifically, observed values for heat ranged from 2.1 to 2.4 sec/vehr the higher (more conservative) figure was adopted.

• Lieberman, E., " Determining Lateral Deployment of Traffic on an Approach to an Intersection", McShane, W. & Lieberman, E.,

" Service Rates of Mixed Traffic on the far Lef t Lane of an Approach". Both papers appear in Transportation Research Record 772, 1980.

• • Interestingly, studies have shown that haat decreases (i.e.

capacity increases) during periods of congestion, relative to that during off-peak traffic conditions. This behavior reflects the fact that motorists are more attentive and are highly motivated to reduce their travel time, during congested conditions. Our estimates do not include this beneficial

,_ effect.

U 42

7s To sum =arize the foregoing discussion:

\) -

e The saturation queue discharge headways, hsat, for through vehicles can be quantified by empirical observation e The turn-movement-specific headways, hn, are then calculated, taking into account the effects of turn movement percentages, link geometry and other factors e With the control treatment prescribed as part of the evacuation plan, the value of Pm may be defined e The capacity for each turn movement is then formed from equation (1).

Capacity Estimation Alone Sections of Hichway The capacity of highway sections -- as distinct from approaches to intersections -- is a function of roadway geometrics, traffic composition (e.g. percent heavy trucks and buses in the traffic stream) and, of course, motorist behavior.

There is a fundamental relationship which relates service volume (i.e. the number of vehicles which can pass a point in a given time period) to traffic density. Exhibit 4 describes this relationship.

As indicated there, the service volume increases as density increases, until the service volu=e attains its maximum value,

(">T

\- Vg, which is the capacity of the highway section. Note that as density increases beyond this " critical" value, the rate at which traffic can be serv:.ced (i.e. the service volume) dagliata below capacity. Thereferia, in order to realistically represent traffic perfor=ance during :engested conditions (i.e. when density exceeds the " crit: cal" value), it is necessary to estimate the service volume, Vp, under congested conditions. This value, Vr, which is less that capacity, V E, should be used for developing the evacuation plan and for encimating evacuation times, whenever congested conditions prevail.

The value of Vy can be expressed as Vy = R - VE where R = Reduction factor which is less than unity.

Based on e=pirical data collected on freeways, we have employed a value of R = 0.85.

We also apply the reduction factor, R, on approaches to intersections. Here, the uso et a factor to lower the value of capacity reflects the possibility that some motorista may respond

(~%

O 43

O O O Service Volume ,- -

Free-flowing Ir. creased Inter-vehicle inter- Stop-and- Higher densities traffic little interactions actions produce Co opera- possible but observed interaction among reduce speeds - disturbances and in- tions with- very infrequently vehicles stable flow crease speed variance. in a queue Some storpages and state a t

- <=ane formations capacit,

' service volume q under congested I, Conditions

. \

\

N N.

\

\

\

\

\

\ '

Traffic Density (veh/ mile)

Exhibit 4. Fundamental Relationehip between Volume and Density

more slowly to police control which, at some times, may conflict

(~; with the control device indication. Such slower responses, which V/ translate into lower capacities, justify the use of Vp instead of VE on all highway segments of the evacuation network, whenever congestion prevails.

The estimated value of capacity, Vg, is based primarily upon the type of facility (e.g. controlled access such as I-95, uncontrolled access such as Route 101D) and on roadway geometrics. Clearly, a winding narrow road has significantly lower service volume than does the Exeter-Hampton Expressway.

Sections of roadway with poor geometrics are characterized by:

e Lower free-flow speeds than on highways with good geometrics.

e Longer headways separating moving vehicles.

The first factor increases travel time when conditions are undersaturated. The latter factor produces lower service volumes, thereby reducing capacity.

The proc.idure used here was to estimate "section" capacity, V,

E based on our observations travelling over each section of the evacuation network and by reference to the Highway capacity Manual. We then determined for each highway section, represented as a network link, whether its capacity would be limited by the "section-specific" service volume, VE or by the intersection-(_,) specific capacity, Qcap,m. For each link, we selected the lower value of capacity.

General considerations Inclement weather conditions and/or lighting, and poor or wet pavement conditions reduce capacity by virtue oft e Lower free-flow speeds reflecting greater caution on the part of motorists.

e Longer vehicle headways reflecting lower traction and/or more cautious driver behavior.

The decrease in service volume due to those factors can bo estimated based on either direct observation or by referencing other studies in the literature.

Arnlication to Seabrook EPZ As part of the development of the Seabrook EPZ traffic network, an estimate of roadway capacity is required. The source material for the capacity estimates presented heroin is centained in:

,_ 1985 Highway Capacity Manual (HCM), Special Report 209 U

45 t

(~'j Transportation Research Board

%> National Research Council Washington, D.C. 1985 The highway system in the Seabrook EPZ consists primarily of three categories of roads:

e Two-lane rural roads e Multi-lane Expressways e Freeway ramps Each of these classifications will be discussed.

Two-Lane Rural Roads Ref: HCM Chapter 8 As a further aid to the estimate of roadway capacity, we have adopted the following four general types of rural roads:

1. " Low" design roads - 10 ft. lanes, 1 ft. shoulders (e.g.

Breakfast Hill Road)

2. " Medium" design roads - 11 ft. lanes, 2 ft. shoulders (e.g. Routes 286, lA N/S)

3. "High" design roads - 12 ft. lanes, 4 ft, shoulders (e.g.

O Route 1)

4. Limited access roads - 12 ft. lanes, 6 ft. shoulders (e.g. Exeter - Hampton Expressway)

General relationship - The relationship describing traffic operations on general terrain segments is as follows:

SFi = 2,800 x (v/c)1 x fdX fw X fRV where:

SFi = prevailing total service flow rate in both directions for roadway and traffic conditions, for level of service i, in vph (v/c)1 = ratio of flow rate to ideal capacity for level of service i, obtained from HCM Table 8-1 fd = adjustment factor for directional distribution of traffic, obtained from HCM Table 8-4 O

46

fy = adjustment factor for narrow lanes and restricted

(~)

v shoulder width, obtained from HCM Table 8-5 fgy = adjustment factor for the presence of heavy vehicles in the traffic stream, which can be computed as outlined in the HCM We have applied these procedures of the 1985 HCM to obtain estimates of the "section" capacities of two-lane roads within the EPZ. An outline of these procedures is presented below.

Note that capacity is defined as the service flow of Level of Service, LOS E.

Based on the field survey and on expected traffic operations associated with evacuation scenarios:

e The two-lane roads within the EPZ are classified as

" rolling terrain".

e Percent no passing zones is approximately 60.

e Directionality of traffic moving over two-lane roads during evacuation will approximate a " split" of 90 percent moving cutbound; 10 percent moving inbound, averaged over the duration of the evacuation.

e Traffic mix is: 1% trucks, 1% buses, 4% recreational

,e-) vehicles during the summer.

(_)

On this basis, the value of v/c of LOS E is 0.91 taken from Table 8-1 of the HCM. The directional split factor, fd is 0.75 from Table 8-4 of the HCM. These factors apply to all four rural road types.

The road width factors, fy, are obtained from Table 8-5 of the HCM:

e " Low" design roads - 0.78 (by interpolation) e " Medium" design roads - 0.88 e "High" design roads - 0.97 e Limited access roads - 1.00 The vehicle mix factor is ba :ed both on the percentages of heavy vehicles and on the Passenger Car Equivalent (PCE) value of each vehicle type. Since PCE is related to vehicle performance, the PCE is lower on higher speed roads. For example, due to sluggish acceleration, a truck moving in local street traffic exhibits a higher PCE than the same truck does when it is on a freeway.

s 47

on this basis, the following values were obtained:

{}

fgy = 0.87 for roads of high, medium and low designs; fHV = 0.91 for limited-access roads The following table represents the two-way and one-way (directional) capacity estimates for the four road types identified:

2-way 1-way Equivalent VE VE Headway Road Type (V/C) fd fw fHV (vph) (vph) (sec)

Low design 0.91 0.75 0.78 0.87 1297 1167 3.1 Medium design 0.91 0.75 0.88 0.87 1463 1317 2.7 High design 0.91 0.75 0.97 0.87 1613 1452 2.5 Limited access 0.91 0.75 1.00 0.91 1739 1565 2.3 Notes: 1. The one-way capacities of roads for evacuating vehicles are calculated by multiplying the two-way -

values obtained from the HCM procedures, by the '

directional split, 0.9.

O 2. These directional (i.e. one-way) estimates will be multiplied by the factor, R = 0.85, when the traffic is moving under congested conditions.

We have obtained hourly traffic counts along several roads from the NH DOT. Included is Route 51 in Hampton. The maximum recorded daily volumes on this road in the summer of 1985 udro well above the estimate of 1739, calculated above. Thus, these estimates of capacity appear to be reasonable.

Freeway Cacacity There are two freeways in the Seabrook EPZ; I95 and I495. A general relationship is used to compute the one-way freeway service flow at different Levels of Service:

SFi = c3 x (v/c)1 x N x fw x fgy x fp where:

SFi = service flow rate for LOS i under prevailing roadway and traffic conditions for N lanes in one direction, in vph (v/c)1 = maximum volume-to-capacity ratio associated with LOS i

()

48

c3 = capacity under ideal conditions for freeway element

() of design speed j; 2,000 pcphpl for 60 mph and 70 mph freeway elements, 1,900 pephpl for 50 mph freeway elements; the value of c3 is synonymous with the maximum service flow rate for LOS E N = number of~ lanes in one direction of the freeway fy = factor to adjust for the effects of restricted lane widths and/or lateral clearances t

fgy = factor to adjust for the effect of heavy vehicles (trucks, buses and recreational vehicles) in the traffic stream

fp = factor to edjust for the effect of driver population 1

Based on the field survey, the Interstate Highways exhibit:

e Essentially level terrain e' Six or Eight lanes o A traffic mix approximating: 1% trucks, 1% buses, 4%

recreational vehicles during the summer

, [. The (v/c) ratio at capacity flow is 1.0 from Table 3-1 of the

(]

v HCM. The lane width factor, fy,.taken from HCM Table 3-12, = 1, ,

for a facility with 12 ft. lanes, 6 ft. shoulder, and.a 6 or 8 '

lane facility.

The. vehicle mix factor, fgy, is computed in a manner similar to that for the rural road segments. The value obtained is fgy =

O.96.

i 1

The final factor, f is designed to adjust the service flow

toaccountfordifferinh,drivercharacteristics. The suggested '

j values (HCM, Table 3-10) range from 0.75 to 1.0 for weekday or 1 commuter traffic. It is expected that during an evacuation, the

,most experienced person in the group will drive. Further, it is I

assumed that virtually all drivers are familiar with the major roads in the Seabrook EPZ. Therefore, a factor fp = 0.90 was l selected.

, m On the basis of these factors, a freeway capacity VE = 1728 vph1 was selected.* So=e indication of this value may be obtained from an analysis of Sunday traffic data on I95, provided i .by NH DOT.

• Vp is synonymous with SFE , as used in the HCM.

1

+

I m

1 49

4 The highest one-way daily volume in 1985 was recorded on Sunday, July 7th': 79,119' vehicles. Unfortunately, we do not O,s have hourly volumes. We can, however, compute the peak hourly flow based on the value of V E:

Peak Hour Volume = 1,728 vpht x 4 la'nes = 6,912 vph This value is only 8.74 percent of the recorded ADT. (Usually, peak hour volumes exceed 10 percent of the ADT.) Thus, even in the absence of hourly data, the estimate of VE appears to be realistic.

This estimate translates into a mean vehicle headway of 2.1 seconds.

Freeway Ramos Capacity.of freeway ramps was assumed to be 1170 vphl. This e is a conservative estimate (see HCM, Table 5-5), and corresponds to a queue discharge headway of 3.1 seconds per vehicle.

Note that the actual capacity for a portion of-the traffic stream on link, i, could be less if its movement-specific headway, hm>hsat as discussed earlier. The estimated values of capacity during congested conditions are reduced belew their respective VE values by the R = 0.85 factor as discussed earlier.

O \

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i o

6. PRELIMINARY RESULTS The Evacuation Times for the specified scenario are shown graphically in Figure 17.

The Evacuation Times may also be tabulated in two formats as follows:

Distance from Elapsed Times at which Indicated Percent of Seabrook Station Subarea Population was Evacuated (HR: MIN)

(Miles) 25% 50% 75% 90% 100%

l 2 1:25 2:35 3:55 4:40 5:45 5 1:20 2:30 4:00 4:55 6:05 10 1:20 2:25 3:50 4:50 6:50 i

EPZ Boundary 1:20 2:30 3:55 5:05 7:00

, . s Distance from Proportion of Population Evacuated at Seabrook Station Indicated Elapsed Times (HR: MIN)

(Miles) 1:00 2:00 3:00 4:00 5:00 6:00 7:00

() Vehicles: 3343 6234 8696 11785 14325 15197 15197  !

Percent: 22.0 41.0 57.2 77.5 94.3 100.0 100.0 l Vehicles: 7331 16202 23681 30220 36364 40183 40268

, 5 l Percent: 18.2 40.2 58.8 75.0 90.3 99.8 100.0 l

Vehicles: 12663 31160 45411 58033 68281 73985 74321

! 10

} Percent: 17.0 41.9 61.1 78.1 91.9 99.5 100.0 i

1 l Vehicles: 14062 35283 53463 68188 79932 87055 89736

! EPZ 4

Percent: 15.7 39.3 59.6 76.0 89.1 97.0 100.0 It is also useful to examine the elapsed times associated with well-defined areas within the EPZ. The following list contains this data for a representative (non-exhaustive) j selection of such areast

()

51 l

i i

l

. in ,a  :-

l V, Cl (_)8 Vehicles Evacuated 100 -- (Thousands)

""9 "

89,736

- - - - EPZ 80 -- ,,,

Ten-Mile l

60 --

/

40 - -  : Five-Mile w

PJ 20 -'

Two-Mile O f:00 2:'00 3200 4I00 Sl00 6 :I00 7!00 Elapsed Time (llr. : Min. )

l l

Figure 17. Elapsed Times f rom Start of Evacuation for Regions within the Seabrook Station EPZ i

l l

Elapsed Time Required to Evacuate the Area within the EPZ Occucants of the Area (HR:MINF

[_/

s_

Massachusetts Amesbury Town 3:40 Morrimac Town 3:05 Newbury Town 3:30 Newburyport Town 4:50 Plum Island 3:50 Salisbury Beach 5:35 Salisbury Town 5:45 West Newbury 3:30 New Hamoshire Exeter Town 3:45 Hampton Beach 5:50 Hampton Town 6:05 North Hampton Town 6:15 Rye Town 5:00 Seabrook Beach 4:40 Seabrook Town 4:45 NOTE: These estimates apply to the occunants of the areas at the time of'the start of evacuation -- they do not include any additional time required for those who occupied other areas to pass through the specified area. For example, the ETE for Newburyport does not include the time required by those evacuating frcm the north to clear this area.

}

All of the above estimates are referenced to the time that evacuation begins. However, the beginning of evacuation may lag, somewhat, the issuance of the order to evacuate. We will calculate this lag as a byproduct of the calculation of the Trip Generation period. This calculation will take place over the next week or two. As an approximation, add 20 minutes to the above estimates to account for this response lag.

The estimates given above are sometimes called " Clear Time".

Adding the response lag translates those estimates into ETE, by referencing all elapsed times to the instant that the order to evacuate is issued.

Again, wa remind the reader that these ETE estimates are preliminary. Refinement of the input stream to reflect new information and the development of more ef fective traf fic management strategies than are incorporated in the current inputs, will probably modify these ETE somewhat. Absent major input changes, these ETE should not change significantly.

O 53

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APPENDIX A l g Glossary of Terms I

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O Appendix A: Glossary of Terms Term Definition capacity Maximum number of vehicles which has a reasonable expectation of passing a given section of road-way in one direction during a given time period under prevailing

, roadway and traffic conditions.

These are estimates which are ex-pressed as vehicles per hour (vph)

Centroid An origin or destination located in the interior of the network.

,~

Content Number of vehicles occupying a section of roadway at a particular p it in time.

() Destination A location in the network, either within the interior or on the periphery, to which trips are attracted.

Entry Node A network node, usually located on the periphery of a network, which serves only as an origin. That is, vehicles are generated and move into the network to travel toward their respective destinations.

Exit t; ode A network node, usually located on the periphery of a network, which serves only as a destination. That is, vehicles which arrive at an exit node are discharged from the network.

A-1 i

O Term De_finition Green-Time to Cycle Time The ratio of the duration of a green Ratio (G/C Ratio)' interval to the cycle length. This ratio denotes-the proportion of time available to service a specified traffic movement on a specific approach to an intersection.

Internal Node All nodes which are not Entry or Exit nodes. Vehicles travel through these nodes from one link to the next along their respective paths toward their respective destinations.

Level of Service An index (A, B, .... E) which is a qualitative de,scriptor of the oper-ational performance of traf fic on a section of roadway, usually ex-pressed in terms of speed, travel time or density. In practice,

{ each Level of Service index is of ten associated with a range of service volumes. This relation de-pends on the type of facility (freeway, rural road, urban street) . Link A network link represents a specific, one-directional section of roadway. A link has both physical (length, number of lanes, topology, etc.) and operational (turn movement per-centages, service rate, free-flow speed) characteristics. 1 Measures of Effectiveness Statistica describing traffic opera- i tions on a roadway network. Mode A network node generally represents a specific intersection of network links. A node has control charac-teristics, L.e. the allocation of service Line to each approach link. l l A-2 l f I

s Term Definition origin A location in the network, either within the interior, or on the periphery, where trips are generated at a specified rate expressed in vehicles per hour (vph). These trips enter the roadway system to travel to their respective destina-tions. Network A graphical representation of the geometric topology of.a physical roadway system, which is comprised - of directional links and nodes. Prevailing r:adway and Relate to the physical; features !. traffic conditions of the roadway, the nature (e.g. 1 composition) of traffic on the roadway and the ambient conditions (weather, visibility, pavement (]) conditions, etc.) Service Rate Maximum rate at which vehicles, executing a specific turn maneuver, can be discharged from a section , of roadway at the prevailing con-ditions, expressed in vehicles por second (vps).  ; Service volume Maximum number of vehicles which can pass over a section of roadway i in one direction during a specified , time period with operating conditions at a specified Level of Service. (The service volume at Level of Service, E, is equal to capacity) Service Volume in usually expressed as vehicles per hour (vph). Signal Cycle, The total clapsed time to display Cycle Time or all signal indications, in sequence. Cycle Length The cycle length in expres sed in secondr,. A-3

O Term Ee_f.i n Lt io_!! Signal Interval A single combination of signal indications. The interval duration is expressed in seconds. In gen-eral, several intervals, in sequence, comprise a phase. Signal Phase A set of signal indications (and intervals) which services a parti-cular' combination of traffic move-ments on the approaches to the in t e rsec t ion . The phase duration is expressed in seconds. Traffic Assignment A process of assigning traffic to paths of travel in such a way as , to satisfy all trip objectives (i.e. the desire of each vehicle to travel from a specified origin in the network to a specified de-(]) stination) and to optimizo some stated objective or combination of objectives. In general, the cb-jective is stated in terms of mini-mizing a generalized " cost". For example, " cost" nay be ex-pressed in terms of travel time. Traffic Density The number of vnhicles which occupy one lane of a roadway section of specified length at a point of time, expressed as vehicles por lane-mile (vpin or vpm) Traffic Simulation A computer rodel designed to repli-cate the real-world operation of vehicles on a roadway network, no as to provide statistics describi'ng traffic performance. These sta-tinticu are called Measures of Effectivenenn. O A-4

l O Term Definition Traffic volume The number of vehicles which pass I over a section of roadway in one

                                                                                                                 ~

direction, expressed in vehicles i per hour (vph) . Where applicable,  ! traffic volume may be stratified i by turn movement.  ! Travel Mode Distinguishes between private auto, l bus, rail and air travel modes.  ! Trip Table A rectangular matrix or table, whose or entries contain the number of trips , Origin-Destination which are generated at each specified ' Matrix origin, during a specified time period, which are attencted to (and travel toward) one of the specified  ! destinations. These values ste ex-  : pressed in vehicles per hour (vph) [ or in vehicles. () Turning capacity The capacity associated with that component of the traffic stream which executes a specified turn  ! maneuver from an approach at an { intersection.  ! i i N l I L I 5 + l ! X[)' t

A-5 '

1-

   .4-4.. A_AWM                        h.h 4

i i  ! 4 1 I J f' l i< i. l 1,  : 4 1 J 1 i 4  : t i ( i i L l , I t i j i l APPENDIX D ' l c Traf fic Assignment Model i C i f l i v r i I l I i [ t l

                                                              \

e l I i Ie , l i l I r I

o V Appendix D: Traffic Assignment Model 1 The traffic assignment program which is employed in this study is an elaboration of an existing model developed by Dr. Sang figuyen.* This model is an equilibrium assignment model which employs mathecatical programming methodology to scarch for, and attain, a globai cptimum solution. The term," optimum" , implies that the solution is unique and that it minimizes a specified cost { function. This cost function, in our application, is exprensed directly in terms of aggregate travel time. That i t. , the raodel formulation relates travel time to the assigned volumes on each network link according to the following formulations fy Y i Ti=To,i l&aC where - - Tg te Travel Time on link, i,sec (pJ T g'g = .Specified link,1,sec free-flow (zero delay) travel time on V = volume of traffic on a link,1, vph Cg = Capacity of link,1, vph a,b = Specified calibration parameters The cost function, then, is formulated in terms of travel tirm along each path f ron each origin to each respectivo destina-tion. Minimizing this path-specific travel tirne (i.e. the so-called (Jaer Optimization) , all vehicles are assured of being routed along the shortest (in travel timo) possibic path to their respective destinations. The corputation.1 algorithm assigns traffic over the network in such a way an to minimize this aggregate cost. That is, the

                                    .illocation of volumes, V, to Lho network links, 1-1,2..., ti is accor pliched in such a way as to:

• tig oye n , S. ar E amet6, f., "T PA IT I C ,,, A n_, Il< pi l l i b r i um T r a t f i c As:+igngynt l' rog r a m , " Publical lon flo. 17, Cent re de Fenorche nur Icb TsarWpontu, March l'J Pi.

11 - 1

l O e Sa t t s f y a l l spec t t ted origin-des t inat ion dernandu , e Satisfy the mintmum-cost (travel time) objective, e Satinfy any specified control treatment and turn re-strictions designed to:

             - Expedite the evacuation process
             - Mintmi::e radiation exposure of the vehicle occupants.

Most applications of traffic ausignment crploy constant, estimated, values ot link capacity, Ci. It is well known, how-ever, that link capacity is a function of many factorn including the (unknown) turn volumes on all links serviced by a common intersection. Consequently, the assumption of const. ant link capacity compromises the efficacy of the assignment results. To resolve this problem, KLD has expanded the existing TRAFFIC model to incorporate a model, nared the TRAFLO CAPACITY model. This model computes accurate estimates of capacity, C1,that are always consistent with the assigned volur.es, V(, on each link. This capacity model consists of three integrated component

• O V e A formulation which calculates the service raten for through and lett-turning vehicles in a lane, given, among other data, the propertion of left-turners in the lanc, e Another formulation for through and right-turner service rates, e A formulation which calculaten the lateral deployment of traf fic on an app * ;ach, yiciding the proportion of through and turning . chicles in each lane.

These three components are exercised in an iterative manner to produce accurate and self-consintent entimates c f service raten for approachen of general configuration and for all types of control devices. Many tests have confirmed that this solution procedure in rapid, accurate and unconditionally con-vergent. In nunmary, the Traf fic Asnignrent t% del used in thin project Teil t D:io n t. n the latent t; tate-of-the-art arvt p rovi tten ,iccurate enti. H-2

t natus of link volumes, stratified by turn movement at the down-stream mode (intersection) . Thow turn volutes on each link are subsequently input into the Traffic Simulation Program. [ t Another output provided by the Traffic Assignment model in the estimated travel tirt.en on each link. These estimates are not particularly accurate--they are usually optimistic--but they do identify the " hot spots" in the networks those links which are i I sove rely congested. This permits the analyst to identify candi-dato solutions flow of traffic, to ro11cvc the congestions and to expedite the  ! i a g 8 i l 1 t ! i

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1 O 1 e 1 I, i f k" r I APPENDIX C j Traffic Simulation Model: I.pyngy I f I I l l l l l i I t l f O  : t~.. i,..... i

Appentlix C: Traffac P i r u l a t. i on thaa f 1. - A U A model, named I -: EV , to an n'a- e st <n ;r Level II simulation meJel, de'.31opod by VLD for th" Federal Highway Administration ( FilWA ) , wi th ext. ens tuna in scope to ac-commcdate all types of facilities. This rM el proJweea an extensive set of output M0! as shown in Table F-1. The traffic stream is described in terms of a net of link-specific statistical flew histograms. These histograms (Figure C-1) describe the platoon structure of the traffic stream on each net-work link. The simulation logic identifies five types of histo-grama:

                                      . The ENTRY histogram which describes the platoon flow at the upstream end of the subject link.                          Thin histo-gram in simply an aggregation of the appropriate OUTPUT turn-movement-specific histograms of all feeder 1 inks.

e The INPUT histograms which describe the platoon flow pattern arriving at the stop line. These are obtained by first disaggregating the ENTRY histogram into turn-movement-specific component ENTRY histograms. Each such component in modified to account for the platoon O at veret " " ten ree"tt= e= trerric tr"ver=e, the tiax-The resulting ItJPUT histograms reflect the specified turn percentagen for the subject link. e The SERVICE histogram which doncribe the nervice raten f or each turn rnovement. These cervice rctes reflect the type of control device acrvicing traffic on this approach; if it in a nignal, then thin histogram re-flects the :,pecified movemont-specific signal phaning. A neparate model was developed to entimate nervice rates for each turn rnovement, given that the control is GO. e The GUEUE histograms which describ" the line-varying ebb and growth of the queue format' ion at the ntop line. These hintograma are derived from the interaction of the respective IN hit,togrann with the SCHVICE hiatagrams. e The OllT hintogrann which <!escribe the pattern of traf-fic dinchargi"9 from th" nubject link. Each of t.h " !!! h i s t og r ar,n i, t r a t u. f ro r.",! into an OUT h i n t oy r .*m by t he control appli"'t to t h. nubject Iin'. l'arh of fheue OUT h i a, t < q r a n s is oil b"! into tho (og is e g it e) FNTPY histogram of its riceiving lin'. C-1

i k - v/ Table C-1: Measures of Effcctiveness output by I-DYNt:V i Heasure pnits i Travel Vehicles-Hiles and Vehicle-Trips Moving tire Vehicle-Minutes Delay time vehicle-Minutes Total travel time Vehielo-Minutes Efficiency: moving time /

total travel time Percent Mean travel time per vehicle seconds l Mean delay per vehicle Secondu itean delay per vehicle-mile seconds /Hilo

                                                              !!can speed                                                                                                                              H11es/ Hour Mean occupancy                                                                                                                           Vehicles Hean saturation                                                                                                                          Percent Vehicle stops                                                                                                                            Percent These data are provided for each network link and are also aggregated over the entiro network.

O C-2

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l i l 0 10 20 30 40 N-  ! l } Time, sec. i 4 t

tioto

IDY!!EV operatos upon the areas onclosed within thoso L i histograms. Thoso areas represent the product of l

! riow Rato and Time (veh/ soc x soc) to yloid the f l number of vehicles ontoring a link, traveling on tho l

!                 link and discharging from the link.

I ' i I t Figure C-1: Statistical representation of the  ; traf fic stream platoon st ructuro. ' I I !O  : ' 1 C-3 i i

tiote that thin approach provides the 14/H' -

                                                                                   .si %1h ?+
                 "" v ' t"""'t'r '"" '""'""'t""

O- specific component of the traffic stream. ' '"'" '"'"-" """""'- Each component in serviced the at a different real world. sa turat ion flow r.ite an in the case in Furthermore, the i- h Y ?J!.V logic will be able to recognize when one component of the traffic ficw in encoun-tering saturation conditions even if the others are not. the Algorithms provide entimates of delay and stops reflecting interaction of the ItJ hintograms with the SERVICE histogramn. The I-DUiEV Icgic also oroei& s for p roperly truatin ap t 11La.. conditions reflecting queuen extending from one link into it s upstream feeder links. l A valuable feature of I-D":JCV in its ability to in t e rn a 11,- generate functions which relate mean speed to density on each link, given user-specified estimates of free flow speed and saturation service rates for each link. Such relationships are essential in order to simulate traffic operations on f ceways and rural roads, wher9 the signal control does not ext t or where ito effect in not the dominant factor in impedin, traffic flow. All traffic simulation rodcIn are data-intennive. O omttime= the iaeet r ea u tre me "t= o r t h e t- or::c " "~ ae t-Table C .' In order to apply the I-DY:!CV !!c u l, the physteil triffic environment must be specified by the user. This inrut data describes e Topology of the roadway nystem o Geometrien of each roadway component e Channelization of traffic on each roadway component e Motorist behavior which, in aqqregate, determinen the operational performance of vehicles in thn nyutom e Specification of the traffic control devle.". and . their operational characterintien e Traffic volumen entering .ind 1 caving the roadway cyntom e Traffic composition To provide an officient f rarework for d"f ining t hene spe-cificationn, t he phys ic.i t envi ronren t in reprer.onted an a not-work. The unidirectional links of the network generally represent roadway componentn either urban ntre"to or freewty negmentn. The noden of the network generally reptonent urban int ernoct lonn of points along the f reeway where a 9"omot r ic pr e!"*t ty chanqen (o.g., a lano dre>p, change in g r+1e or r amp. ) J C-4

i () Figuro C-2 is an examplo of a network representation. The freeway is defined by the sequence of links, (1,2), (2,3), ..., (5,6). Links (8000,1) ,and (7,8002) are Entry and Exit links, 3 respectively. An artorial extends from node 7 to node 15 and is partially subsumed within a grid network. I The developront of the I-DYNEV nodcl followed directly after DYNLV was comoleted. The perceived need for I-DYNEV was based

)           upon the requirement for a model having all the demonstrated capabilities of DYNEV, but one which consuned less computer tino and storago.

The major distinction between DYNEV and I-DYNEV is that the latter model directly calculates the intocral of the histograms l described earlier (sco Piqure C-1), insteed of comouting the amoli-tudos of each histocran slice, as does DYNEV. One other differenco j is that in I-DYNEV, vehicles which cannot travol along their assigned ovacuation route due to excessivo congestion will divert to another, alternative evacuation route Rf the latter is not congested. In I all other respects, the two uo'dois are either identical (e.g., the input and output software) or are very similar, with any differences { reflecting the major distinction described above. i () This major distinction results in software code which consumes significantly less storage for I-DYMEV than for DYNEV, reflecting i the climination of large arrayn containing the amplitude values of each histogran slico. The reduced computational burden is roflected in almost a three-fold reduction in comouting time. ' A thorough comparison was mado betwoon the ETE results generated oy the two models. It was found that all pairs of results, DYNEV and I-DYNEV, woro virtually identical for a wide variety of network i configurations and traffic demand lovels. Note that the two models l require the samo baule input stream and produce similar output i formats. i On the basis of those results, I-DYNEV is used exclusively for the EESF system, to calculato evacuation tiro estimatos, and was used Station. to calculato the Evacuation Timo Estimates (ETE) for Soabrook O C-5

q Table C-2: Input requirements for the I-DYNCV Model b CEOMETRICS Links defined by upstream downstream node numbers. Links lengths. Humber of lanes (up to 6). Turn pockets. Crade. Network topology defined in terr.s of target nodes for each receiving link. LRAFFIC VOLUMES On all entry links and sink / source nodes stratified by vehicle types auto, car pool, bus, truck. Link-specific turn movements el 0-D matrix (Trip Table) O 'a^rrte co"Taor setetr'ca'to"= Traffic signals: link-specific, turn movement specific. Control may be fixed-time or traffic-actuated. Stop and Yield signs. Right-turn-on-red (RTOR) . Route diversion specifications. Turn restrictions. Lane control (i.e., lane closure).

                   . DRIVER'S AND OPERATIONS CHAPACTERISTICS Driver's (vehicle-specific) response mechanisms:     free-flow speed, aggressiveness, discharge headway.

Link-specific mean cpeed for free-flowing (unimpeded) traffic. Vehiclo-type operational characteristics: acceleration, deceleration. Such factors as bus route designation, hus station location, dwoll time, headway, etc. C-6

i l Entry, Exit nodes

                                           ,                            are nuttered in the form, exXX s_

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e i O Oi i l l . APPENDIX D h Detailed Description of Study Procedure k.

t P i O i Appt....x D: Dotalled Description of Study Proceduro This appendix describos tho activition to bo performod in order to produce accurato estimates of ovacuation timos on the Evacuation Planning Zone (EPZ) for a nuclear power plant. Tho individual stops of this effort are reprosented as a flow diagram in riguro D-1 Each numbered stop in the description which follows corresponds to the numbered clo-ment in this flow diagram. Stop 1 The first activity is to obtain census data $ defining the spatial distribution of population within the i EPZ. Specifically, obtain the population in each of 160 '  ! cells of a polar grid which is contored at the nuclear station, I and consists of 22.5' sections and rings spaced one mito { apart. Transient population characteristics must also be ' estimated on the same basis. I Stop 2 The next activity is to examino a large-scale map of the EPZ. This map onables one to identify the access

• i roads from each residential development to the adjoining [

olomonts of the analysis roadway notwork. This information  : is necessaary in order to assign yonorated trips to the L correct links of the network. This map also enables ono ( to reprenant the geometrics of complex intersections proporly [ in terms of their network configuration. [ f Stop 3 With this information absorbed, the next stop i is to conduct a physical survey of the roadway system within the EPZ. The purpose of this survey is to determino the necessary measuromonts of roadway longth and of the number of lanos on each link, the channolization of thoso Lanos, whether or not there woro any turn restrictions or special treatment of traf fic at intersoctions and to gain the noc- [ essary insight required for estimating roatistic values of roadway capacity. At each major intersection, tako note of f l the traffic control devico which was innta11od. In addition, dotormine whuthor or not, under omorgoney evacuation condi- [ r tions, it would be possible to employ pavnd shoutdors as an additional lano in the ovent such additional capacity was f required.

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cet De mjrapnt; tuta _ II 2 Study Large-Scale

                                     !1ap of Un;:

If 3 Survey the Roadway i Systen within the E."%

          ?
                                              'I              4 Develop !!ctwork Representation O!
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(-. .b I:stimato Link Cacacitien and Locato _ Centroids (_ _ f' Create the snput Stream for the Traffic Annittnment ?tndel _._.'[-__.. Debug the 7 Innut S t t <

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                                  ==

Execute the Traf fic Assignrent

                                                   'tod e l  ._

U 9 Examine Traffic _=- Assignnent Output (Itcrate) L Desults are Changen

                                                       /         Satinfactory      D Needed P_          10 Develop Control Treatments t

v; and/or flodify Trip Table to Improve Resulta Y 11 Modify the Input Strean to Roflect the Changen of Step 10 rieju r o t)-1. Fl ow Di.istriin of A< t ivi t ion (con t . ) I v D-3

l \ l f"N m/ n I 12 Corplete Innut Strean for the Traf fic Sinulation flodel by Incoroporating Traffic Assignrent Outputu F __ 13 Executo the

                      - . -          Simulation '4odel            ,"
                                            -.-6I
   ,_s                                            Y             14

( ) Exanino Traffic Sinulation Output flo Changos to 17 ( f terato) h Satisfactory E ISOS p-U #" "" - Changes - Assignment 909ults Nooded Outnuts _ __ _ Y. . 15 Develop Control Troatnants and/or Modifv Trin Table Thono qunults to Improve Romults Reficct Changos flado in Stop 15 N V ftodify the Input Stream }, to Hoflect the A Changen of Stop 15 , I I l" # E " I l' 6 epa r o twl, l'inu D i .op r.m o f Ar t i v i t le s (enne l . ) D-4 #

1 .- Ns . , j r i

                                                /     . s
                                         ?              .
                                                                                                           /

O# i \ Step 4 With thia information, develco the evacuation 1

             '          ~ ~
                                                 ; : network re;3rosentation of the phyateal roadway mistem i                    '                              ' gigg_J.

the capacieten atWith the network drawn, proceed to estimate  ; [ - whCrc each link and to locate the centroids "at:d en,en enter the analysis network. trips would be generated du

                                                              . Step            6
                         .                                                                             With all the information at hand, it ta time to' porform Traffic     Assigneent'Model. the e f fort of creating the input strcan for the 3         c                                                                                  This model was designed to be
                                                .,, ompatible with the Traffic Simulation Model used later in the project, in the sense that the input format required for one quired   model by the       was        other,           uncirely conpatible with the input format re-This step in the procedurethus avoiding duplication of ef fort.

this input la labor-intensive. Fortunately, ! rado can stcoam need only be developed once; any changes ) - be impicmented quickly and at Whall cost. Thus, itiin possible to execute these models on different scenarios

                                      "             with nt roam  very        little offcet needed to modify the basic inout to represent the specific attributes of each scenario.

O St99 7 executo the Traffic Af torAssignment creating theModel. input stream by using PREDY:1, This computer program contains upwards other imprut.or input. This diagnostic software producou of 1,000 diagnostic inconsistencies and any messages which assist the user in identifying the source of

                                             - the        problem and cuide the user in preparing the necessary coaractions.

Stop 6., With the input the Traffic hsaignment Model. stream f ree of error, execute is a very officient software codo. The Traffic Asnignment program Stop 9 statistien produced by the Traffic AssigneontThe program. nextThic activity in to is a labor-intensivo activity, requirina the direct partici-pation of skilled experience to interpret engineers the who possens the necessary practical causes of any problems reflected resultsinand the to result.determino the O D-5

, {) Essentially, the approach i ., to identify those " hot spots" in the network which represent locations wher.:c con-gested conditions are extreme. It is then.necessary to identify the cause of this congestion. This cause can take many forms, either as excess demand due to improper routing, as a shortfall of capacity, or as a quantitative error in the way the physical system was represented in the input stream. The examination of the Traffic Assignment output leads to one of two conclusions: e The resul*:s are as satisf actory -as could be expected at this stage of the anal sis process, or e Treatments must be introduced in order to improve the flow of traffic. This decision recuires, of course, the application of the user's judgment based upon the results obtained in previota s applications of the Traffic Assignment Model and a conpa rition of the results of this last case with the previous ones. In. the event the results are satisfactory, in the opinion of the user then the process continues with the exercise of the siru-() v lation model in step 12. Otherwise, proceed to Step 10. Step 10. There are many " treatments" available to the user in resolving such problems. These treatments range

• from decisions to reroute the traffic by imposing turn restrictions where they can produce significant improvements in capacity, changing the control treatment at critical intersections so as to provide improved service for one or more movements, or in prescribing specific treatments for channelizing the flow so as to expedite the movement of traffic along major roadway systems or changing the trip table. Such " treatments" take the form of modifications to the original input stream.t We then perform the modifications to the input stream, reflecting the control treatments described above. As indicated previously, such modifications are implemented i quickly to the extant that more than one execution of the computer program is possible in a single day.

Step 11. As noted abere, the physical changes to the input stream.must be implemented in order to reflect the changes in the control treatments undertaken in Step 10. At the com-(~T pletion of this activity, the process return, to Step 8 where

 \_) the Traf fic A :signment Model is once auain executed.

n-6 {

F Step 12. The output or the Traffic Assignment Model includes the computed turn movements for each link. If the () s user is executing the Traffic Assignment and the Traffic Simu-lation models in a single run, then this data is'automaticall, accessed by the latter model. If the simulation model is executed separately, the user must modify the input stream for the Traffic Assignment model by beginning in the turn-movement data, using PREDYN. Step 13. After the input stream has been debugged, the i simulation model is exec.uted to provide the user with detailed estimates, expressed as statistical measures of effectiveness (MOE) , - which describe the detailed performance of traf fic operations on each link of the network. Step 14. In this step, the detailed output of the Traffic Simulation Model is examined in order to identify once again the problems which exist on the network. The results of the simulation model are extremely detailed and are far more

     '                          accurate in their ability to describe traffic operations than those provided by the Traffic Assignment Model.          Thus, it is [

possible to identify the cause of the problems by carefully studying the output. Again, one can implement corrective treatments designed () to expedite the flow of traffic on the network in the event [o , that the results possible are considered to be less efficient than is to achieve. i In the evenr that changes are needed, the analysis procese proceeds to Step 15. On the other hand, if the results were satisfactory, then one can decide 1

              '                whether it is necessary to return to Step 8 to execute the
                                                       ~

i Traffic Assignment Model once again and repeat the whole process, or to accept the final results as being the "best" ) that can budget betime and achieved within the reasonable constraints of allotments. Generally, if there are no [- changes indicated by the activities of Step 14, then we can conclude that all results were satisfactory, and we can then proceed to document them in Step 17. Otherwise, we have to return to Step 8 in order to determine the effects of the changes implemented in Step 14 on the optimal routing patterns over the network. This determination can only be ascertained by executing the Traffic Assignment Model. Step 15. This activity implements the changes in control treatments or in the assignment of destinations associated with one or more origins in order to improve the flow of traffic over the network. These treatments can also include the consideration of additional roadway segments to the existing analysis network l O . o_7

       ,sy-

in order to disperse the traffic demand and thus avoid the {~) x/ focusing of traffic demand which can produce high levels of congestion, Step 16. Once the treatments have been identified, it is necessary to mcdify the input stream accordingly. At the completion of this effort, the procedure returns to Step 13 to execute the simulation model once more. Step 17.. The simulation results are then analyzed, tabulated and graphed. The results are then documented, as required. 4 a o j _ o-a L

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  )                                                                                    APPENDIX E c

[ Literature Review and Data Compiled to Date h 1, i f l O l l l l

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_ .-. . m. - APPENDIX E: Literature Review and Data Compiled to Date 1. State of New Hampshire, Dept. of Resources and Economic Development, Division of Parks and Recreation. Records are kept of the number of parked vehicles at all State Parks. Park attendance is imputed from this data by assuming an average value of vehicle occupancy: Park Attendance Based on 3.5 Persons ner Vehicle Summer, 1983 Summer, 1984 Seacoast'Recion (13 weeks) _(13 weeks) Ft. Constitution 2,000 Hampton Bathhouse 2,000 204,200 170,700 Odiorne 18,200 Rye Picnic Area 19,500 15,900 15,100 Wallis Sands 99,300 70,000 Wentworth-Coolidge 1.500 1.300 341,100 278,600

2. Town of Hampton, Department of Public Works [

one way of estimating chances in population, is to measure the flow of sewage. As indicated below, the highest daily flow l occurs over the July 4th Weekend. i Average Flow per Day (millions of gallons) 1964 1965 July 4th Weekend: 3.61 2.77 (four days) All of July:- 3.21 2.63 Excess.on July 4th Weekend Relative to Month of July: 12.5 5.3 (percent)

3. Hampton Beach Traffic Study: Report compiled by Dufresne-Henry, Consulting Engineers, for the Town of Hampton, N.H.; date August 1, 1984 The following statistics have been culled from this report:

a) Approximately 3400 people use the State Park beach j (adjoining the bath house) on a peak day. b) Off-street parking area for this facility is approximately 1200 spaces. ( . l E-1 l m

7- . - c) Other off-street parking lots included: Church St.: 400 cars Ashworth: 580 Island Path: 315 Casino: 530 Playground: 230 Median: 453 Ocean Blvd. N.: 139 On-street: 187 TOTAL: 2834 d) Cites a N.H. State Planning study which estimates that the beach (other than that near the bath-house) can accommodate 15,600 people at high tide and 31,300 people at low tide. Including the State Park would raise these estimates to about 19,000 and 34,700,.respectively.

4. Regionwide Systems Performance Study Update: Working Paper proposed by Merrimack Valley Planning Commission, dated July 1985.

This report presents the results of capacity analyses - conducted on the heavily travelled roads within the MVPC region.-

• The . Level of Service (LOS)-describing traffic flow conditions is presented for all surveyed roads in each town:

Percent of Roads with Level of Service Town & A g D. E E Newburyport 84.7 3.8 11.5 0 0 0

         -Newbury             100.0      0           0        0      0       0
,         Merrimac            100.0      0           0        0      0       0 Amesbury             82.7      8.7         4.3      4.3    0       0 Salisbury            86.5     '8.1         0        0      5.4     0 West Newbury        100.0      0           0        0      0       0 The term, " Level of Service" (LOS) is defined as follows:

LOS Definition A Free flow. Users virtually unaffected by others in the traffic stream. l i B Stable flow. Presence of other users in the traffic ! stream begins to be noticeable. Freedom to select desired speeds is relatively unaffected. C Stable flow. Users significantly affected by interactions with others in traffic stream. Selection of speed affected by presence of others. O E-2

D High density, but stable. Speed selection and freedom ()- to maneuver are severely restricted. E Usually unstable. Operating conditions at or near i capacity. Speeds are low, but relatively uniform. Small increases in flow or minor perturbations can cause j breakdowns in traffic flow. I F Forced or " breakdown" flow. Traffic. demand exceeds ' capacity; queues are formed and stop-and-go operations result.

These definitions are adapted from the 1985 Highway capacity Manual.

l -

                                                                           .According to this study, driv 9rs in the indicated towns j                                                                      generally enjoy traffic. conditions which are free flow or stable.

This condition implies that there is substantial reserve capacity available, relative to normal peak period traffic demand. The lone exceptions are in Salisbury ~where Route 1 intersects Routes 286 and 1A/110, respectively. A subsequent study (see below) indicated a LOS of F for the latter intersection during peak hours. 4 l 5. Salisbury Center Traffic Study: Draft Report prepared by l MVPC, dated March 1985. l ' )('}

         \v This stu'dy focused on traffic conditions on the approaches to Salisbury Center -- the intersection of Routes 1, lA and 110.

Traffic counts were taken during the summer of 1984; peak hour traffic volume data is given below: f ! East or West or Road North-Bound South-Bound e Rt. lA: Beach Road, E-W 1473 910 Rt. 1: Lafayette Road, N-S 576 693 Rts. 1, lA: Bridge Road, N-S 1647 1173 Rt. 110: Elm Street, E-W 842 900 Three alternative designs were submitted to the Town of Hampton; one of the these was found to be acceptable, with some modification. This choice involved, among other factors, l channelizing Beach Road as a four-lane road in.the vicinity of l L the. intersection and installing a signal there. j 6. Economic Impact of Certain Shoreline Users on the New Hampshire Coastal Zone, prepared by the Southeastern New Hampshire Regional Planning Commission, dated October 1975. This study acquired data using observers stationed along the access roads to the beaches. Observers also conducted surveys on the beach and along Ocean Blvd. Aerial photographs were taken and the. vehicle population was estimated from these photos. {} E-3

() The data was stratified by four coastal areas:

1. Not Sandy: Hilton Park, Great Island Common, Odiorne's Point, Rye Harbor' State Park-

2. South Sandy: Seabrook, Hampton State Beach, Cottage Beach, Hampton Beach

3. Mid Sandy: Noth Beach, Plaice Cove, Little Boar's Head

4. North Sandy: ' Rye Beach, Jenness Beach, North Beach, Wallis Sands Car occupancies and number of parked cars were recorded on days which ' represented " good beach weather":

Location: Not South Mid North Sandy Sandy Sandy Sandy Avg. Car Occupancy (persons): 3.2 3.5 3.0 3.1 TOTALS Cars Counted Weekday: 103 7,496 1,005 897 9,501 Weekend (max. of - Sat. & Sun.):. 639 8,269 2,053 1,689 12,650 Est. Pecole on Coast Weekday: 300, 25,900 3,000 2,900 32,100 Max. Weekend day: 2,100 28,600 6,200 5,400 42,300 Percentaces Overall 3.5 73.8 12.3 10.4 100.0 Day-Tripper 83 44 51 55 45 Vacationer 17 56 49 45 55

7. Parking Analysis using Aerial Photographs KLD was provided with 9 sets of color-slides containing aerial views of the entire coastal area within the Seabrook EPZ, from southern Plum Island on the south to the Portsmouth area on the north. Each set consists of approximately 55 slides, representing one sortie along the coast.

Each set was analyzed by employing a slide projector and a i screen.. For each set, the number of vehicles parked within the EPZ along the beach areas was determined, together with an estimate of parking capacity, t

'                                All vehicles sighted on the. film were counted, including those in designated parking lots, in unmarked (and unpaved) lots used by parked vehicles, along the curbs, in driveways and in

,' backyards and on front lawns. Capacity was estimated by: e Counting the parking stalls in all marked parking' lots L O E-4

     ,,_..,c.-,--._,-,m.,~r            - - - - - - - - - - - ex- m -*-""T'*      -m-""*****

e Measuring the length of unoccupied curb space and ( _) . expressing this distance in terms of cars e Assuming that all open, accessible lots could be occupied to their full extent l e Assuming that private driveways,. front yards and backyards would be utilized for parked cars Many of these unpaved lots are at significant distances frem.the beach (1/2 to 1 mile) and are thus relatively unattractive. Other locations (e.g. on Plum Island) are attractive only to those who seek relative solitude; in a pratical sense, such capacities are overstate'd i We believe that these capacity estimates represent a reasonable upper bound to the number of possible parked vehicles in the indicated areas 1 The' statistics describing actual parked vehicles presented below for each section of the coastal region, are derived from aerial filns taken on Sunday, August 11, 1985 in the early. afternoon. These estimates represent the highest vehicle counts of any of the available sets of photographs. The weather on that day was described as clear, with temperature approximately 90. degrees -- ideal conditions'for attracting day-trippers to the beaches. Estimate of Parking Vehicle Count Coastal Section Cacacity (cars) (} (all vehicles tvoes*) Plum Island (MA) 2830 1440 (51) Salisbury Beach (MA) 8060 5800 (72) Seabrook Beach (NH) 2650 2280 (86) Hampton Beach (NH) 7770 5720 (74)

        . North of NH 51, incl.

North Beach (NH) 1300 990 (76) Plaice Cove, Little Boar's Head, Bass Beach (NH) 600 500 (83) Rye'& Jenness Beaches

         & Straw Point (NH)                1440                    950    (66)

Wallis Sands, Odiornes Point (NH). 820 540 (.6 6 ) Totals: 25,470 18,220 (71.5)

         *Few buses were observed; RV's constituted less than 2 percent of the total count. -values in parenthesis are vehicle count as percent of capacity.

E-5

I

8. Beach Population Analysis using Aerial Photographs Separately, KLD obtained 3 aerial photographs of Hampton Beach which were 11" x 17" in size. These photographs were taken on July 4, 1983. .It was agreed.by all officials interviewed that 1983 was the peak year for beach attendance and that the Fourth of July weekend appeared to attract just about the heaviest i crowds of the season. Both assessments were supported by data '

describing traffic counts at permanent State counters (ATR's) and by examining sewage flows (see item 2, above). l On this basis, we' concluded that by counting individual 4 people on the beach for that day, we would have an empirical basis for an independent estimate of beach population. Review of these photos revealed that: 1 e The most. crowded portion of the beach was opposite the  ! 4 casino, which was also closest to the sanitary facilities maintained by the State. i e Few people, relatively speaking, were observed off the

                  ~

beach at that time of day. Traffic flow was extremely light and only a handful of people were visible inland of the beach. ^ 3

                            .Both observations were confirmed by N.H. Parks Dept.                             ;

personnel as consistent with their experience. i A large-scale photo of this beach area was examined and, with the help of a magnifying glass, a' count of people was undertaken. A check confirmed that a total of about 1160 persons were on the beach and on the abutting sidewalk near the State facilities. A subsequent measurement of this beach area using a measuring wheel indicated a total area above the high-tide line of approximately 75,500 sq. ft. The photos also revealed that few, if any, blankets were spread on the seaward side of the high-tide line: the sand was wat throughout the low tide period. (At i other locations, the sand drained and people did spread their towels on the seaward side of the high-tide line.) We thus computed the practical upper bound of people density on dry beach area to be approximatnly 65 sq. ft. per person. We are not suggesting that a higher density is not attainable. There were, in fact, small clusters of higher density groups of people. Rather, this data indicates that a substantially higher density at Hampton Beach is not likely to be realized, when large areas of beach are considered, given the l current limitation on available parking capacity. l These photographs also indicated that the density of [' population on the beach, outside of the area in front of the casino, was somewhat lower than the figure given above. We o E-6

therefore believe that use of the 65 sq. ft. per person, applied

 . (~)         to the entire beach area in Hampton Beach, will overstate the A>          actual population, somewhat.

9. 1983 Beach Area Traffic Count Program: Seabrook Station EPZ, report prepared by HMM Associates, dated February 1984 This report describes the results of a comprehensive traffic count program undertaken during the summer of 1983. Hourly, directional traffic counts are provided at each of six locations extending from just north of NH 51 on Route 1A, to just west of the amusement area in Salisbury, on Beach Road (Rt. lA). This area includes Hampton, Seabrook and Salisbury beaches.

The report acknowledges that the data cannot be used to estimate the number of vehicles within the beach area at a given time (p. 3-4). Yet data is presented which indicates that on the peak day (July 16th), the maximum accumulation relative to 4:30 A.M., was about 9,000 cars at 2 P.M. (On a 24-hour basis, the maximum accumulation was about 6,200 cars.) The maximum rate of accumulation over one hour was less than 2,000 vehicles. The estimate-of peak accumulation of transient vehicles used in the HMM evacustion study was 12,900, some 43 percent higher than the peak value of 9,000 measured. HMM also estimated some 10,400 vehicles belonging to permanent, seasonal and overnight persons, for a total of 23,300 vehicles. In 1982, the peak accumulation was about 7,400 vehicles. (s Peak, two-direction traffic volumes (veh/hr) were (approx.): Route 1A, Hampton: 1700 Route 51, Hampton: 1700 Route 1A, Seabrook: 2100 NH 286, Seabrook: 1600 Route 1A, Salisbury: 1500 Bridge Street, Salisbury: 1800 A total of 19,400 vehicles exited these beaches over a 6 1/2 hour period. The peak hour volume was just under 3,900 vehicles. Of course, this figure is probably below the aggregate roadway capacities and should not be interpreted as an upper bound. Weekday traffic was about 75 percent.of weekend traffic. l

10. Beach Capacity Analysis for Shoreline Areas Around Seabrook, New Hampshire, prepared by EMM Associates, dated June 1982 This study investigated beach usage and capacity characteristics. The coastal area considered lies between the Parker River National Fildlife refuge on Plum Island, Mass.,

north to Concord Point, north of Rye Beach. However, data was presented for both Wallis Sands State Park and Odiorne Point State Park, both of which are north of Concord Point. E-7

l '~h Total annual attendance at the four coastal State Parks generally ranged between 250,000 and.300,000, with a long-term trend that was essentially flat between 1970 and 1981. The annual attendance at Hampton Beach State Park ranged from about 150,000 to 180,000, in general. Vehicle parking capacity was' estimated at 19,212 vehicles. This capacity included parking lots and on-street curb parking, but may not have included driveways and backyards. Studies of population on the sandy beach areas yielded observations consistent with ours (see item 8). Specifically, careful analysis indicated that a perfunctory assessment of beach population would yield overestimates of beach density since, at any time, many beach towels are unoccupied. It was also determined that the casino area of Hampton Beach was of particular interest. The study conducted a " peak day - peak area" analysis of beach density. Specifically, sampling areas, upwards of 3,500 sq. ft. were selected which represented "the most crowded section within the (beach) segment", thus providing an upper bound of beach density. The-report cautions the reader not to assume that such peak values are applicable to the entire beach segment (p. 3-13, 14). For example, while the peak density for the major Hampton Beach segment is 44 sq. ft. per person, an average <~N density calculated using six sampling " plots" was 62 sq. ft. per (_) person. (This figure compares with 65 sq. ft. per person measured by KLD -- see item 8.) The observation is made that even on peak days over a 3-year study period, "several parking lots ... are not filled to capacity". The total parking capacity was estimated at about 19,200 vehicles. On this basis, assuming 3.3 person per vehicle, a " realistic" capacity estimate of 63,400 persons is offered.

11. Roadway Network and Evacuation Study (for) Seabrook, New Hampshire, prepared by Wilbur Smith and Associates, dated December 1974 This report describes the assumptions used, the highway capacities estimated, the traffic management techniques to be applied and the evacuation time estimates (ETE). Several evacuation scenarios are considered.

The analysis methodology is not described in any detail. It appears to be based on an assumed speed of travel for each roadway, the estimated roadway capacities and estimated traffic demands. In the " controlled sector evacuation" it is assumed that people in some of the designated sectors will wait patiently for other sectors to be evacuated, before beginning to evacuate (Fig. E-8

 s . )

i COM*JTER #1 Cut. 3* tnTdtTI.it #J Col. 35 t ut.. J A wl.. Il 1 3 Minutes Or Les.: 1 46 - 50 Minutes i 5 'finutur or l' It. - A Minut s i J 6 - 10 Minutes 2 51 - M Minute

• Ic*. 2 55 Minut.4 j 11 - 15 Minutes 3 Sh - 1 Hour 2 6 - 10 Miuntos 1 So - t H..or 4 lta - Ju Minutos 4 over 1 Hour but i II - 13 Mie.utes 5 21 .*5 Minutus 4 over 1 H. o r .

Ivss than 1 4 16 - 20 Minutes to t 1s == i n .u tp 26 - 30 Minutes Hour 15 Minutes 5 21 - 25 Minutes 1 Hour 15 7 31 - 35 Minutes 5 Setween 1 Hour 4 26 - 30 Minutes Minutes 8 36 - 40 Minutes 15 Minutes and  ? 31 - 35 Minutes 5 Between 1 Hour 9 41 - 45 Minutes 1 Hour 30 Minutes 8 36 - 40 Minutes 15 Minutes 6 setween 1 Hour'31 9 41 - 45 M1 cutes and 1 Hour 30 Minutes and 1 Minutes Hour 45 Minutes 6 Between 1 Hour 7 8etween 1 Hour 46 31 Minutes Minutes and 2 and 1 Hout 45 Hours Minutes 8 Over 2 Hours 7 Setineen 1 Hour 9 46 Minutes O and 2 Hours X Dun't Ibiow/Refune.4 8 Uver 2 Hours 9 - 0

• X unn't Know/ ~

l'efused CWMTER O

                                                            ~                                                                  COMMt.TER P6 CUL. 33                             COL. 39                         COL.40                           COL.41 g   j                    1 5 Minutes or Less              1 46 - 50 Min.ates                1 5 Minutow or v                      2 6 - 10 Minutes                                                                                    1 66 - 50 Mtrutes 2 51 - 55 Minuts u                        Lene                     2 51 - 55 Marut n 3 11 - 15 Minutes                3 56 - 1 Hour                     2 6 - 10 Minutes                 3 % - 1 itoar 4 16 - 20 Minutes                4 ove r 1 l'au r. Su t            3 ' 11 - 15 ::inutes 5 21 - 25 Minut s                      less than 1                 4 16 - 20 11;t.ates 4 .ovr 1 Haur.

6 26 - 30 Minutes b. . t ! s stun Hour 15 Minutes 5 21 - 25 Minutes  ! !! cur 15 7 31 - 35 Minutes 5 Between 1 Hour 15 6 26 - 30 Minutes Minutes 8 36 - 40 Minutes Minutes anJ 1 7 31 - 35 Minutes' 5 l'etween 1 thair 9 41 - 45 Minutes Hour 30 Minutes 8 36 - 40 Hanutes 15 Minute, 6 Setween 1 llour 9 41 - 45 Minutes and 1 Hour 31 Minuree and 30 !*in'. ten 1 Hour 45 Minutaa

• betwesn i Heus 7 5ctween 1 Hout 46 11 M1rintes

                                                                            'tInuts. aaJ 2                                                        and 1 Hw r hours                                                                 45 Minutv-8 Ovsr 2 itnuts                                                    7 1%tss en 1 hour 9

16 Minute. 0 nd 2 Hourn X Dun't Know/ Ref uw.! $ ove r 2 Hour 9 0 X Don't F,now / ItsfasvJ 1C. If Cumut ?r #1 were nottfled of an castgene) at the $sabro.4 Station while at work ur college, wou1J that person rs turn lome? (kfprAT QttST10:. N K F Alit CO.%TF:4.) CotttTEM 81 CO'twtrna #2 (WM TU -) fiM?LIFk '. O'L. 42~ ~ ~ E iP TO COL. 4T SKIP Tu n.ot . 46 n 1P To ( Oi. , 4 } yt t;;p 1 Yen icA 1 Yes 1GA 1 Ye* - 10A 1 is s tw 2 Ns

• 103 2 Nc 105 2 Na 108 *o . IMh

( 3 %t U.re-10A 3 Not sur.- 10A 1 Not sure _.1CA 2 3

                                                                                                                                                    *..sc
                                                                                                                                                        %, r e-l C A
                                  .       . . .   - - _ _ .                    _ _ . - ~              . - .               -            - - . ~              -

F-3

                                                              -u. .           ~"   ' ' ' ~ ~ - - "         '-~G"*~~~~~                    ~ ~ * "

T~ ~ ~ ( ,. == [ i L./ 3 IOA. how teng would it t.ake Corunuter 88 to remplete prepJr.ititu f or traving w.erk or cottese prtor to starting siv t r ig* In me .' (Mt.Pl.AT (JL'EST!o.N NN (J( H OF;1 t f R. ) (110 NOT NTAD ANSWI.HS.) CUM'tTER e t Col.. Ae. OeTTTER 82 ull . 4 7 CaH. 4M l 5 Minuten Or  ! 4fi - % Minuttw I 5 Minuttu i ut.. W t.t ss 2 it - % Mt uute4 1 46 - W !!! nut. f)r 1.c .s 2 it *,,Minut. J ei - 10 'ftnutem 4 % - Itlour 2 f, - 10 Minutes 3 II - 15 Minutv4 4 over L litur. a % - 1 ilour 4 16 - 20 Minutum 3 11 - 15 Minut m 4 over i Hour. teu t I m than 4 16 - 20 Minutts 5 21 - 25 Minutes t Hour 15 5 21 - 25 Minutos but is** ti .a 6 26 - 30 Minutes Minute. I li.a.t 15 7 31 - 35 Minutes 6 26 - 10 Minutus Minutes 5 Butween 1 Hour 7 31 - 35 Minutes 5 Setween 1 Hour

                     & 36 - 40 Minutes                           15 Minutes           8 36 - 40 Minutes 9 41 - 45 Minutes                                                                                         15 Minutes and I llour          9 41 - 45 Minutes                        and 1 Hour 30 Minutes                                                    30 Minutes 6 Between 1 Hour                                    .

6 Between 1 Hour 31 Minutes 31 Minutes and 1 Hour .ind 1 Hour 45 Minutes 45 Minutes 7 Between 1 llour 7 Between 1 Hour 46 Minutem 46 Minutes anJ 2 Hourn and 2 Hour. S Over 2 ti.inr. 6 over 2 Hours 9 y s' O n X ik.n ' t kn..m/ X pan't krawl RefuerJ Pcfused CCM"ift l.R e l

                                        *~                                                           r W TTER a4 e5          Col. 50                            cot, 31                                           '

j i .5 Minuten or 1 46 - 50 Minute. Cut .51 I 5 Minutu (.cl .51

                                                                                                                   ! 46      - M Minutes Lvsa                       2 51 - 55 Minuter 2 es - lu Minutes                                                        or I. css             2 51 - Si Min utes 3    58. - 1 I!our           2 6 - 10 Minutsu               3 56 - t Hour 3 It - 15!!!nutes                  4 ove r 1 Itou r.             3 11 - 15 Minutes 4 th - 20 Minutes                           but le 3 t'ul. . %

l' Icgs Th.tn 15 winate ~ I'~l H.*ure 1.* I Ik u t g 15 Minutte 2 li - 30 winutes

• 1 Itours 16 Minures b 1 Hourg 3 31 - 45 Minutes 30 Minutes 4 4h Ninutes To I Hour 1 3 litmre 11 Minutes Ta 3 Hours 3 1 Hour to I Hour 15 winutes 45 Minutes 6 I Hour 16 Minutes to 1. Hour 4 3 Hours 46 Mtputes to 4 Hours 30 Minutes 5 4 Hours Ta & Fours 15 Minutes 7 1 Hour 31 Minutes To I Hour 6 4 Hours 16 Mtputes to 4 Hours 43 Minutes 30 Minutes 8 1 Hour 4h Minutes To 2 Hours 7 4 Hours 31 Minutes To 4' Hours 9 2 Hours to 2 Hours 15 Minutes 65 Minutes 0 2 Hours 16 Minutes To 2 Hours A 4 Hours 46 Minutes To 3 Hours 30 Minutes * $ H.wrs to S buts 15 Minutes X 2 Hours 31 Minutes To 2 Hours 0 5 mmre 16 Minutes to 5 Hours 45 Minutes 30 Minutes Y 2 H mts 4h Minutes to 3 Hours N 4 Hours 31 Minutes to ) Hours 43 Minutes T 3 H.urs 46 *linutes to a hourg C.11. ,15 1 hm*t Know

   ' ()              Thank yod **fV        ""Ch*                                                           ~
                                                   --~yg.j s. 3m g"c. urn) f'% l  .

I ( 'D r-s

i._. r _ , , . - ~ . a s. , . _ _ _ _ . - -- - A" *~-

• e w >~~= s *~= " w i - * ' ~ ~ ~ ~ * = = ~- '
• Table 1 ,;

Town Eschense' latervieve latervieve Number Per Sachenae Per Coenuaity Population Percentaae i j Portsmouth 430 134 134 14325.50 10.3% Portooeuth 431 164 134 Newcentle 14325.50 10.)) 9 936.00 .70 Greentend 21 2229.00 1.60 Total Intervieve , Enchangee 430, 431 2 94 ~ t Sye 964 80 , 46 4876.00 3.50 North Neoptoa 34 1571.00 2.60 trentwood 642 101 to test Elageton 1082.00 .30 12 1179.00 90 Kingston 46 Newton 4797.00 3.50 33 3490.00 2.50 trentwood 679 10 10 1042.00 .40 Emeter 772 86 SS $449.50 4.20 sessiettos 6 731.50 Newfleide .50 8 448.00 .60 strathan 14 South Heepton 1444.50 1.10 3 327.50 .20 Esoter. 778 78 11 5449.50 6.20 Kensinston 6 731.50 Strathae .50 14 1448.50 1.10 South Neopton 3 327.50 .20 Seabrook 474 60 60 6398.00 4.60 i Naepton 926 117 i 101 11271.00 0.10 Maepton Falls 14 1465.00 1.10 i West Newbury 363 27 27 2848.00 2.10 4 Norriesc 346 44 44 4778.00 3.40 Salisbury 462 104 29 L Newburyport - 1038.50 2.20 75 8025.00 3.40 Salisbury 465 ISO 29

• Newburyport 3038.50 2.20
• 75 4025.00 S.80 Newbury 46 4897.00 3.50 Aseebury 388 14 3 14 3 1$176.00 11.00 TOTALS 1300 1300 134535.00 100.00 r

i 1 O . l F-6 i I

                                                                                ^                                                     , _ . - _ . _ _ i

   -      .. -                 .             . . ~      ...~.1              -.1            - . . . . . .   , , . - - - .    .. -.

s . . Table 2 Teluphone Enchangen Ta le Sampled alrittnal New Number Of Sample Sanple Telephone Int e rviews

                            %84e                   Stao             [ychange         Per Exchange        Commualty.

! %sw Hampshire } (Area coJs 603) 307 298 430 134 i

• Par t smou t h All 164 Po rt s mou t h l Acw Castle i

Creenland j 84 80 944 83 i Bye. North Hampton lol Ill 642 101 Brentwand East Kingsten Kingston ' Newton 679 10 stentvocJ I ' 170 164 772 86 Eserer i

                                                         .                                                rensington 2:ewfielde Stratham South llaertcon 779               74              Eneter Kwnsingten 9tratham e                                                                                                          Sou t h P.imrt on i

124 179 474 60 9eabrock 926 119 Hampt on 4 Maepton Falls i tu s eact.u se t t s

   ..                    (Area C.ds 617)

( 79 71 36) 27 West Kewburv [ 346 44 Merrimac 283 254 462 104 Salisbury f' Newburyport 465 150 Salisbury l Newburyport I. I Newbury [ J,',4 , [4 ] 188 16) Aprebury t D ;/A I lr* 1100 1100 1300 e I 1 4 I I i ( [ ' F-7

 , - -    -     _ _ - - 2.
            .__            .. m                 _ , .      . .               ,. . .

O s i { l

                                                                                   .                   i APPENDIX G                                            i P

Tabulations of Telephone Survey Data 1 r I l 9 , l-f t i l l I ! I i L I i l f l [ t l r h

                                                                                                      .i

[, f 1

  +We--.                                 ____nym-e r m wem nw     w~ew h* w -m*

 , ( m,
   \ J' P"4%C'U ;F4 nCL!tb;LC v5 CARS PE4 FC'JiC1CL vE> SONS P24                       4Ur?!4 C: CAR 3 PER 4 G L' 3 9 ,", L O                                  /'
         '4 00 acn*L1                          C         1       2               4
         ------...                                                      3                             TCTal 1                TCTAL:       25      147       13        0 PJRC?AT:

1 11e 13.4 72.0 7.3 c.0 0.5 g;;.a

                                                                                                                    /,

2 TCTAL: 9 156 246 21 10 45C PERCIAT: 2.0 34.7 54.7 e.4 2.2 100.0 3 TCTAL: 1 49 132 45 IT PERCEAT: 243 04 19.9 54.3  !!.5 7.0 l00.C 4 . TCTAL: 1 46 124 41 31 247 i PiACCAT: C.4 13.6 31.6 16.s 12.6 100.C 5 TCTAL: 0 15 54 26

                              *E1 CENT:

11 1kt q m. C.C 14 2 SC.9 24.5 10.4 10C.; 6 TCTat 0  ? g 13 11 31 PtRCSAT: 0.C 17.9 33 3 2d.2 20.5 1CC.C 7 TCTAL: 0 1 1 1 1 6

     ,                       P!' CENT:      C.0     25.0    25.0    25.0     2" 0                    100.C       '

s TOTAL: 0 0 0 2 2 4 PERC?NT: C.0 00 C.0 50.0 50.0 100.C 9 TCTAL: 0 1 2 0 1 . PE1 CENT: 0.0 25.0 50.0 0.0 25.0 10 0.0 <

             .:                 TCTAL:          0        5       ?       I       4                        1:

PEnCENT: C.C 41.T 11.7 23 J3.3 1c;.C bb G-1 k

       -,.a.                    .... . ... . .. ..-. w m                   .
                                                                                         .. .~. a. w                 _-           - . .w .           ..
   -    s               ,

Pct;;,\3 PL' M C'J 3 ;b C L ') V5 3CF0;L C H L ;R .\ ?!t F C .3 ; H ;'. ; c' Ac63.\0 Pit huk CK JF SC.mLOL C u l L J t ; P. oC4 MCUIde:LO / .* C J a i ,q L') 0 1 2 3

                                                                                                                .          !        $       TJTAL i                       ..... ...                                          ...     ...       ... ...           ...        ...      ...       .....
 '                                1                    TOTAL:             134          0        %         0        C          0        0        !!$
 .                               >                 PCRCENT:          100.C        0.0       :.C      0.0     0.4        C.0       0.;       1":.0
                              's                                                         *
                                  ?
                                    "                  TOTSL             424         17         2        0        C           1        0        4 4 -'

P E 8 C E 'J T 95.i 2.9 C.4 0.0 00 0.2 C.c 1:0.0 3 TCTAL* 11C ICS 1J 0 C 3 0 P'9 Ci*'T t *  ? 53.1 42.4 41 .0 00 0.3 00 100.0 4 TETAL**. oc

~                                                                                   c3      123          1        0          0        C        24' P iR C E *!T :      24.3 25.? 49.8               6.4       00         0.0     'C.0        1 3.0 TOTtL                 12     21        34       31         0           0
     /^^                                          P f 3 C;', T :

C 105 U 12 3 15.? 32 1 ?$.f C.0 0.0 00 132.1 S TOTSL* i 3 c 9 I? C '; 3; PSRO2NT! 20.5 7.7 13.4 23 1 33.3 00 0.0 101.0 7 TCTAL2 C 0

                      '                                                                       2         2        0          0        0             4 PE4C:NT:             0.0       0.0 50.0 50.0              00        0.0       00         10C.0
                               ?                     TCTAL:                   1      0        2        0        C           1       0             c PlP Ci'4T t        2 *i . C    C.0 50.C         C.:       C.0 25.0            0.0        100.0
                            ,'                       TCTAL*                   1      9        0        0                   0 1                   2             4 p i A ctiN T :      25 0        C.0       00     0.0 25 0            0.0 50.0             132.C 10                        TOT Al

• 1 0 1 1 1 0 1 IT P 2 H C L*8 71 59.2 7. 7 0.0 7.7 7.7 00 '.' 107.9 O

G-2

                                     - _ . - - . _ _ _ . . , < -      . . . - - .         ~v.              -
                                                                                                                        -        c- --      A -
                                                                                                                                           .s a   ,

I 1 LO - 33457AS DCR 5US#b2L PJ VL a'?i'(*.D eTACHG;fl$ P{? HOU;iMCLO' s P i?. S ';N S P E '< dCUSCH]LJ BUFBIt OF CAYS PE4 $UMMet

                ~~~-~~-~~

C 1-5 6-10 11-15 IS+ T3?:L

                                                                                        ... .....            ....a        .....       ....              .....

1 T; T A L': 90 44 ' 16 24 1El P14CCAT: 4c.7 24.3 3.9 d.1 13.3 100.C

2 TCTAL: 152 113 36 43 56 44C

[ SCRCEhT: 43.6 ' 25.7 9.7 S.9 12.7' 100.C 3 s

         ;                                                            TCTAL:               75         J3           21           15 PERCENT:            32.3 3*              232'
          !                                                                                        35.8           S.1          4.5    16.4           10 0. C ~
         }

4 TCTAL: si a7 29 19 27 .242 PcRCENT: 33.6 36.0 11.6 7.9 11 2 100.C t '\ 3 T ; T t.L 36 37 , 7 6 13 I C 4' PEPCihT: .34.0 25 6 S.7 5.3 17 3 100.C k 6 TCTAL: 12 14 7 3 3 39 l PC2CCNT: 35.9 7.7 30.9 7.7 17.9 100.C 7 TCTAL: 2 0 1 1 0 4 PCRCENT: 50.0 0.0 25.0 25.0 0.0 100.C 9 TCTAL: 0 0 1 0 3 4 i PEo.CiNT: 0.0 0.0 25.0 00 75.0 100.C 4 TCTAL: 2

• 1 0 0 1 4 PEPCEhT: 5C.0 25.0 0.0 0.C 25.0 100.C i 1 10 TCTal 1 4 0 0 1 13 P2RCSNT: 61 5 30.3 C.0 0.0 7.7 130.C 4

i , ( i ( ' G-3 i i. L

_ .. a. x - .. . n,

                           .   . -.                                                     ~-      ~.. ~ . - =- A l l- ~ -= , w ~ ~ ';=-w=*~~-            -

s

  ,\_

rm *

   'w/

P C.4 3 C A P:4 h,'L".d L; 'i 3 0 ; v.* J T ' A 3 P "1 9Cr;; *:LO P k3LNS 964 h FuiF OF CO:iFUTER3 POR HCU;2 HOLO WC'Ji!HCLU C 1 2 3 4 T :T il 1 TCitt: 93 103 0 0 0 7:4CE;.T: 186 44.c 55 4 J.J 00 00 100 0 ^ TOTAL: 157 113 169 C 0 P;9Cf\T: 37.3 441 25.2 37 5 0.C 0.C 110.0 3 TCTAL: 3C 81 97 37 0 245 P G A C E.N T : 12 2 33 1 39.o 15.1 00 100.0 4 TOTAL: 23 95 ed i 23 13 ditCENT ' 247 5.3 29.5 35.6 9.3 7.3 100.C 5 T O T.t L : 2 45 36 13 4 ('i ?CNCEsT: 10. V 75 42 5 34.0 12 3  ?.3 100 6 TCTAL: 2 6 20 6 5 39 P22,0 INT: 51 15.4 51 3 15.4 12 8 1C0.C TOTAL: 0 1 2 C 1 4 DERCENT: C.C 25.0 50.0 00 25 0 100.0 3 TcTAL: C 0 1 2 1

• pdpCEsT: C.c 0.0 25.0 50 0 25.0 100.C o

TCTAL: 1 0 1 2 0 4 P : ?.C E ?.T : 25.C C.0 25.0 50.C 0.0 100.0 10 TCTa' : . 3 2 1 1 4 12 Piac5s?: 31.t 15 4 7.7 7.7 30.3 100 0 G-4

                                    - - ~ - - - , - - . - - - - - , - -                 .-        ,                --.

O f

                     .)
                                                                ' ':F

• L i f.1 T:svil TIFC3 : : T t.'; ;.*, 9 n f 3 0-4 ;; e, 3 f. , ; u 2:

T17; .4 A N 31 (IN .!.\UTi3) A 'J v i ; - CF CC *LT R5 a 2 7. C NT .*F CCdwJT~91 1 5 253 15,5 10 273 15,4 11 - li 27C 13,3 16 - 20 174 t;,3 21 - 25 06

                                        ;6 53 30                                                                   154                                                 o,-

s31 - 35 42 25 30 - 4C 57 3,4 41 - 45 30 5,2 4s - SC 31 t,q 51 - 55 9 3,r SS -

                                                              $C                                                                    QC                                                5,4 61               -

75 32 4,9 73 - 1C 2e 1,7 71 - 105 i c.5

133 -

12C 4 a.; 1 1+ 3 0,5 , VAAIES 7  ;,4 V 'M N C W N ?7 2.g I i l l l 1 G-5 l _ - - - . , - ,_c - - -.n. - . - - - - - - - . - ~ - - - - - - - - - - - - - - - ---,-r----~~------- - - ~ ~ - - - ~ ~ '

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_ _ . ~ . . _ . . . . . . . . . . . , - _ _ . , v. . . . . . . e I I t i j i APPENDIX }{ l i 1980 Census Data 8 e e i t I-(4

                                                                                    -q b                                                                             O,-

i O  ! t Number of Itouseholds with School Indicated Number of Enrollment Vehicles Available - Nursery Other 0 1 2 23 ' t N!! I Rockingham Cty. 2603 41758 3657 24439 26858 10997 1

                                                                                          ?

Brentwood E. Kingsten Exeter 306 2183 360 2002 1327 500 Greenland I' liarpton 200 1960 305 1760 1593 428 i llampton Falls - Kensington [ Kingston 87 912 53 448 563 351 New Castle Newfields = Newtown 97 672 71 "'281 415 240

d. North Ilarpton 57 793 24 428 494 261 Portsmouth (city) 444 4993 1261 4561 2860 742 Rye 59 852 25 612 817 268 Seabrook 18 944 86 1106 834 368 South Hampton Stratham 39 626 13 223 398 171 MA Amesbury 303 2800 663 2248 1627 528 Merrimac 65 1069 86 642 537 260 i Newbury 58 1000 54 444 752 338 Newburyport 410 3151 875 2642 1796 579 Salisbury 81 1394 158 880 754 h 265 ;I West Newbury 39 761 32 221 430 181 Il P

e

0 O O

                         % Worked                                        Avg.

Outside 1 Occupancy Mean Area of  % in  % Using Public Driving Journey to Travel Residence Carpools Transportation Alone Work Time Nil Rockingham Cty. 43.9 23.8 1.4 66.6 1.17 22.7 Brentwood E. Kingston E.seter 49.9 22.0 1.7 61.7 1.18 20.2 Greenland

       !!ampton             67.6     24.2 llampton Falls 2.3         65.8      1.18      22.5 Kensington Kingston             75.3     22.8         0.7          70.0      1.16      21.5 y     New Castle w     Newfields Newtown              91.7     29.7         0.6          63.2     1.24       26.0 North flampton       83.5     10.5         1.4          77.3     1.07       23.9 Portsmouth (city)    38.8     25.0         2.8 Rye 57.1     1.20       14.5 80.9     12.6         1.4 Seabrook 77.2    -1.08       21.0 55.3     23.1         1.1   i. 70.0     1.16 South flampton 21.3 Stratham             77.9     23.5         1.3         67.8      1.17       19.4 MA Amesbury             61.7    26.1 Merrimac 1.0         63.7      1.21      20.6 77.6    27.6          1.4         62.3      1.22      22.5 Newbury               83.4     19.0         4.0         72.2      1.13      25.6 Newburyport          51.5    24.3 Salisbury 2.2         58.6      1.21      22.4 78.6     20.9          1.0         67.4      1.16      20.7 West Newbu ry         87.8     21.5          0.8         71.3     1.16       26.8

A ~ l O O O l Year  % With  % With One Round 5 or Occupied or More  % of Families Single llousing More llousing Vehicles With Children Unit Units Units Units Available Under 6 Years Structure Nil Rockingham Cty. 69375 14.8 65951 94.5 Brentwood 23.6 51162 582 543 E. Kingston 482 370 363 Exeter 315 4406 13.7 4189 91.4 21.1 Greenland 3092 733 705 Hampton 640 4437 24.1 4086 92.5 18.1 llampton Falls 483 2711 462 429 Kensington 450 434 Kingston 407 1518 7.2 1415 96.3 20.0

    =    New Castle              352                                                 1320 335                                  310 O    Newfields               280              274 Newtown                                                                       235 1073    14.2     1007      92.9         28.6 North Ilampton                                                                897 1255      4.9    1207      98.0         17.6          1123 Portsmouth (city)     9877    23.5     9424      86.6        28.2 Rye                   1812                                                 6610 8.2    1722      98.5         17.3          1570 Seabrook              2523   30.1      2394     96.4         17.1          1601 l         South Hampton          221               216

Stratham 844 205 1

1.1 805 98.4 20.4 735 MA Amesbury 5429 29.0 5066 Merrimac 86.9 25.2 2678 l 1572 7.3 1525 94.4 -26.7 ( Newbury 1210 1666- 5.2 1588 96.6 15.5 Newburyport 1449 6259 17.7 5892 65.1 19.9 l Salisbury 3524-2156 7.1 2057 92.3 West Newbury 21.9 1619 882 0.6 864 96.3 20.3 832 l L l 1

O ,f . ij Household

--                                          Persons in Size   Group Quarters
!f       NH                        2.84        2779 Brentwood              3.14          298 E. Kingston            3.13             -
 ;          Exeter   .

2.59 208 Greenland 3.02 - Hampton 2.54 109 Hampton Falls 2.97 - Kensington 3.05 - Kingston 2.90 2 New Castle Newfields ' Newtown 3.05 - North Hampton 2.83 14 Portsmouth (city) 2.63 1431 i

          ' Rye                   2.61              12 Seabrook              2.47                3 South Hampton Stratham              3.10 O .                                            11 MA Amesbury              2.70          380 Merrimac               2.92                1 Newbugy                2.85                2 Newburyport            2.63          425 Salisbury              2.82            58 West Newbury           3.31                1 l

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