Anchorage Travel Model Calibration and Validation

Transcription

1 Final Draft Anchorage Travel Model Calibration and Validation Prepared for Anchorage Metropolitan Transportation Solutions (AMATS) February West Northern Lights Boulevard Suite 601 Anchorage, Alaska ANC/TP41158.DOC/

2 Contents Section Page Introduction...I-1 Background...I-1 Document Organization...I-2 Important Definitions Trips, Trip Origins and Destinations, Trip Productions and Trip Attractions...I-2 1 Travel Model Overview Basic Process Traveler Market (Household) Segmentation Trip Purpose Stratification Time of Day Periods Travel Mode Stratification Transportation Networks Travel Model Structure Land Use and Demographic Inputs Land Use/Density Inputs Household Segmentation Trip Distribution Time of Day Mode Choice Traffic Assignment Summary Traveler Market (Household) Segmentation Household Size Household Income Workers per Household Auto Ownership Children per Household Trip Generation Home Based Trip Production Models Non-Home Based Trip Production Models Commercial Vehicle (Truck) Trips Trip Attraction Models Special Generators/Visitors Other Special Considerations Validation Results ANC/TP41158.DOC/ III

3 CONTENTS 5 Trip Distribution Time of Day Factoring Mode Choice Home Based Work Mode Choice Home Based Shop Mode Choice Home Based School Mode Choice Home Based Other Mode Choice Non-Home Based Trip Mode Choice Validation Results Traffic Assignment and Volumes Cordon Crossings Facility Type Comparisons Comparisons of Statistical Performance Individual Link Performance Summary and Conclusions Appendix A B C D E F Variable Dictionary Anchorage Travel Model Socioeconomic Database Model Feedback Process Commercial Vehicle Travel Model Special Generators Hotel/Motel Visitor Model Screenlines Figure 2-1 Model Overview Flow Chart Average Household Size vs. % Total Households by Size Group Zonal/Regional Income vs. % Total Households by Income Group Average Workers/Household vs. % Total Households by Group Survey vs. Modeled Trips by Community Council Area Survey vs. Model Trip Length Frequencies (Home based work) Survey vs. Model Trip Length Frequencies (Home based shop) Survey vs. Model Trip Length Frequencies (Home based school) Survey vs. Model Trip Length Frequencies (Home based other) Survey vs. Model Trip Length Frequencies (Non-home based work) Survey vs. Model Trip Length Frequencies (Non-home based nonwork) Maximum Desirable Deviation in Total Screenline Volumes Anchorage Bowl Screenlines Statistical Comparison of All Available Measured and Modeled 2002 Weekday Traffic Volumes IV ANC/TP41158.DOC/

4 CONTENTS Table 3-1 Comparison of Household Size Model Results (2002) and 2000 Census Proportion of Household Size Group (Totals for Anchorage Area) Comparison of Household Income Model Results (2002) and 2000 Census Proportion of Household Income Groups (Totals for Anchorage Area) Comparison of Household Worker Model Results (2002) and 2000 Census Proportion of Household Worker Groups (Totals for Anchorage Area) Auto Ownership Model Variables and Coefficients Comparison of Household Auto Ownership Model Results (2002) and 2000 Census Proportion of Household Auto Ownership Groups (Totals for Anchorage Area) Home Based Work Trip Rates (Workers/HH by Income Group Home Based Shopping Trip Rates (Household Size by Income Group Home Based School Trip Rates (Household Size by Children/HH) Home Based Other Trip Rates (Household Size by Income Group) Special Generators Total Trips by Trip Purpose (Survey-Model Comparison) Average Trip Length (in Minutes) by Trip Purpose AM Peak Period Trip Purpose and Directional Factors PM Peak Period Trip Purpose and Directional Factors Off Peak Period Trip Purpose and Directional Factors Complete List of Mode Choice Variables (All Purposes) Home Based Work Mode Choice Model Variables and Coefficients Home Based Shop Mode Choice Model Variables and Coefficients Home Based School Mode Choice Model Variables and Coefficients Home Based Other Mode Choice Model Variables and Coefficients Non-Home Based Work Mode Choice Model Variables and Coefficients Non-Home Based Non-work Mode Choice Model Variables and Coefficients Survey vs. Model Mode Shares for Home Based Work Trips Survey vs. Model Mode Shares for Home Based Shop Trips Survey vs. Model Mode Shares for Home Based School Trips Survey vs. Model Mode Shares for Home Based Other Trips Survey vs. Model Mode Shares for Non-Home Based Work Trips Survey vs. Model Mode Shares for Non-Home Based Non-work Trips Survey vs. Model Mode Shares for All Purposes Summary of 2002 Weekday Cordon Counts/Volumes and Differences Summary of 2002 Weekday Facility Class Counts/Volumes and Differences Percent Difference Targets for Daily Volumes for Individual Links ANC/TP41158.DOC/ V

5 CONTENTS VI ANC/TP41158.DOC/

6 Introduction This document summarizes the development, calibration, and validation of a new travel demand model for the Anchorage, Alaska urbanized area. The travel demand model is a primary tool of the Anchorage Metropolitan Area Transportation Solutions (AMATS) planning organization in its analysis and continuing development of the area s Long Range Transportation Plan. The report summarizes the features of the new travel demand model, its internal submodels and linkages structure, data inputs, relevant major assumptions, and, the tests that were performed to validate and measure the performance of the model in reproducing current travel behavior and transportation system usage. Background The design of the travel demand model takes advantage of new data resources describing the household characteristics and travel characteristics of the Anchorage area population, as well as supplemental datasets assembled from government agencies, the Anchorage business community and other sources. Primary resources used in the development of the new model include the following: 2002 Anchorage Household Travel Survey; 2001 PeopleMover Transit Rider Survey; 2000 US Census and Census Transportation Planning Package (CTPP); 2002 Student Enrollment and Public School Attendance Database U.S. Military Base Personnel and Active Armed Forces Data 2002 PeopleMover Transit Rider Counts 2002 Municipality of Anchorage (MOA) and Alaska Department of Transportation (ADOT) Traffic Counts; MOA Planning Department Demographics and Residential Use Inventories; Alaska Department of Labor Wage & Salary Employment Inventories (ES-202 Unemployment Insurance Dataset) MOA and ADOT Road Characteristics Inventories PeopleMover Transit Schedules and Route Maps. These data were used to create a new model of weekday travel in and around the Anchorage area. The new travel demand model can be used to test a range of assumptions regarding changing land use patterns; new and updated transportation projects and ANC/TP41158.DOC/ I-1

7 INTRODUCTION facilities; and, new or modified public policies regarding land use, and transportation services and facilities. The new travel model design was developed in consultation and with input from local transportation experts and the general community. It is structured to be a responsive as possible to the expected range of questions and issues that Anchorage expects to face regarding its transportation system through Document Organization The documentation is divided into nine sections. The first section, Travel Model Overview, discusses the basic features, capabilities and dimensions of the model. The second section, Travel Model Structure, provides more information on the actual modeling process and further details some of the topics in Travel Model Overview. The third section, Traveler Market (Household) Segmentation specifically documents the set of models developed to estimate residential market segmentation and provides data on their ability to reproduce 2002 actual conditions. The fourth section, Trip Generation, documents the models used to estimate likelihood of trips either being produced and/or attracted to specific developments and geographic locations. The fifth section, Trip Distribution, discusses the models which were developed to predict general trip patterns and how well they match 2002 known conditions. The sixth section, Time of Day, illustrates the basis used for segmenting daily travel estimates into time period specific estimates. The seventh section, Mode Choice, documents the models and the assumptions used to translate general estimates of travel into those, which are specific to mode of travel. This also details tests and comparisons of model prediction results to 2002 actual travel mode shares. The eighth section, Trip Assignments and Volumes, compares the output of the model in terms of specific traffic volumes for different roads and corridors to independent traffic count data collected in the field. This comparison is evaluated against accepted industry guidelines for travel demand model performance. The ninth section summarizes the results, findings and recommendations arising out of the model calibration and validation work. Important Definitions Trips, Trip Origins and Destinations, Trip Productions and Trip Attractions Common nomenclature is used within the travel modeling community to characterize travel and trip-making. Several definitions are defined below: Trip: A trip is a one-way journey from a point of origin to a destination for a specific purpose, i.e., a home to work journey. Unless otherwise specified, trip means a single person journey. Trips include walking and bicycling (referred to as non-motorized trips) when these are the primary modes of travel. Motorized trips include driving or riding as a passenger in various vehicles autos, trucks, buses, school buses, etc. Sometimes trips will be defined as a mode journey, as in a vehicle trip which is used to represent a vehicle driver journey or a transit trip which applies to a transit passenger journey. Trip Origins and Destinations: Each trip has two end points -- an origin and a destination. I-2 ANC/TP41158.DOC/

8 INTRODUCTION Trip Productions and Attractions: It is often useful to relate trips to the trip-maker and his/her residence location. Accordingly, all trips which start or end at the trip-maker s home are defined as Home-Based Trip Productions at the home-end and as Home-Based Trip Attractions at the non-home trip end. For trips which neither start or end at the tripmaker s residence, the origin is defined as the Trip Production and the destination is defined as the Trip Attraction. ANC/TP41158.DOC/ I-3

9 INTRODUCTION I-4 ANC/TP41158.DOC/

10 SECTION 1 Travel Model Overview The Anchorage area travel model is designed to provide forecasts of prospective usage of the transportation system given a wide variety of assumptions about the available network and service and service cost policies that may be assumed for future conditions. To do this, salient characteristics of the traveling population and the available services must be measured and then translated into statistical models and abstract representation. The goal here is to capture, as well as possible, the significant determinants of traveler response to changes in the system and policies. 1.1 Basic Process The framework for this process is travel activity during a typical weekday. To provide effective models that are sensitive to the range of differences in the characteristics of both the travelers and their travel opportunities, the problem is broken down into specific travel market segments. This section of the report discusses the key market segmentation definitions and assumptions used in the Anchorage model. A brief introduction to the modeling process itself is provided here; the model process is presented in significantly more detail in the remainder of the report. To estimate travel demand, the modeling process is broken into six basic steps. These are: Household Disaggregation Estimation of the socio-economic groups or market segments that prospective travelers belong to Trip Generation Estimation of expected tripmaking rates for each of these segments for a given set of tripmaking purposes Trip Distribution Estimation of expected origin-destination patterns for the defined trip purposes and by a set of time periods Time of Day Segmentation Estimation of the shares of origin-destination trips expected for defined time periods and stratified by trip purpose Modal Choice Estimation of the shares of origin-destination trips expected for separate travel modes for each market segment, trip purpose and time period Trip Assignment Estimation of the expected routes (by mode) for the mode specific interchange sets defined by Modal Choice Understanding how these steps are applied requires a picture of how all of the above segments are defined. ANC/TP41158.DOC/

11 TRAVEL MODEL OVERVIEW 1.2 Traveler Market (Household) Segmentation For the Anchorage travel demand model, several separate and distinct submodels were developed to segment households in each subarea (i.e. traffic analysis zone) into five different types of groups. These groups provide breakdowns of the data into different household sizes, range of household incomes, as well as, groups representing different numbers of workers per household and auto ownership groups. The specific defined segments are listed below. Household Size one, two, three, four or more persons Household Income less than $40K, at least $40K, but less than $70K, $70K or more Auto Ownership zero, one, two, three or more vehicles Workers in Household zero, one, two, three or more workers Children in Household zero, one, two, three or more children With the exception of the auto ownership classes, all market segment proportions are derived from the estimated average value for the traffic analysis zone. This segmentation is used to estimate trip generation and mode choice components of the travel demand model. Cross products of segmentation pairs are used to identify more specific market segments. For instance, all two-person households which own two autos. The specific combinations of segments used for the trip generation and mode choice models are identified and described in those sections of this document. 1.3 Trip Purpose Stratification Travel models recognize that trips made for different purposes have different characteristics. To improve accuracy of predictive models, specific models are estimated to represent each defined trip purpose. In the case of the Anchorage, such stratification is used in the trip generation, trip distribution, time-of-day factoring and mode choice submodels. Application of trip purpose stratification recognizes that the basic rates of trips produced at origin locations and attracted to specific destinations; the expected length of trip; the time of day a given trip is made; and, the relative likelihood of using a particular travel mode (e.g. driving an auto versus being a passenger versus riding the bus) are all influenced by the purpose of the trip. The Anchorage travel model has six person trip purposes and two additional commercial vehicle (i.e., truck) trip purposes. These six person trip purposes are: Trips between home and work (Home based Work HBW) Trips between home and shopping (Home based Shop HBS) Trips between home and school (Home based School HBSch) Trips between home and any other type of destination (Home based Other HBO) Trips between work and other places besides work (Non-home based Work NHBW) Trips between two non-home/non-work locations (Non-home based Non-work NHBNW) Two separate trip purposes represent light and heavy commercial truck movements. 1-2 ANC/TP41158.DOC/

12 TRAVEL MODEL OVERVIEW 1.4 Time of Day Periods Another consideration in travel demand model design is determining what periods of the day will be separately forecast. Modeling traffic by time of day has the advantage of linking traffic volume estimates more closely to determinants such as road capacity, transit service availability, likely times for trips of specific purposes to be made, as well as, such influences as time dependent road use restrictions (e.g. peak period high occupancy vehicle restrictions) and road operation (e.g. reversible lanes). Modeling time periods also provides more directly usable information for road and traffic engineering applications; transit planning; and, for estimating vehicular emissions and pollutants. The Anchorage travel model stratifies travel into three weekday time periods. These are: AM Peak Period (7:00 AM-9:00 AM); PM Peak Period (3:00 PM-6:00 PM); Off Peak Period (12:00 AM-7:00 AM and 9:00 AM-3:00 PM and 6:00 PM-12:00AM) Time period stratification is applied after trip distribution and before mode choice.. The traffic assignment module (which places expected traffic on the most likely routes given road congestion) computes adjusted travel times due to traffic congestion during each time period. These adjusted travel time results are fed back to trip distribution to recalculate origin-destination patterns. Trips for all time periods are distributed to each time period. Then, after each cycle of trip distribution, each time-period-specific trip distribution output is factored by it s time-of-day proportion and directionality percentages to yield trips occurring in each time period. The actual proportion of trips in a given time period is calculated after each cycle of trip distribution. 1.5 Travel Mode Stratification The mode choice models utilize market stratification and trip purpose stratification to predict traveler choice of travel mode for every trip. Travel mode in turn is used to define what routes and services are utilized for the ubiquitous transportation network and it s specific modal network components. The Anchorage mode choice model considers the following modal options: Auto drive alone Auto drive with passenger Auto passenger Public Transit/School Bus Transit Walking Bicycling In the case of transit modes, all trip purposes except home-base school are estimated as public transit trips. Home based school transit trips are estimated as school bus trips. ANC/TP41158.DOC/

13 TRAVEL MODEL OVERVIEW 1.6 Transportation Networks The key to understanding how estimated travel demand effects the condition of the transportation system is the definition of the networks that represent linkages between different area locations. In travel models, most often this is represented as a set of interconnected links that approximate the physical layout of various area transportation facilities including highways, streets, exclusive transit facilities, railroads, sidewalks, trails, etc. Expected speeds of travel along these links is used to estimate how long it may take to get between two places via a given mode and route. Bus travel times consider such things as waiting for a bus, transferring between buses, walking to and from the bus stop, and, or course, time spent riding the bus. The objective in defining these networks is to represent how the traveler will see travel options and choose between them. As noted earlier, the Anchorage travel demand model s process is applied separately for three different time periods. The model maintains information about potential paths for in each time period. Potential trip attributes such as auto travel times, frequency of bus services and alternate bus routing patterns are all stratified by time of day. Travel by walking and travel by bicycle are assumed at constant average speeds for all three time periods. The Anchorage travel model transit service, includes a computational submodel that adjusts the speeds and travel times of buses operating in mixed flow traffic with automobiles, based on the level of congestion and operating speeds experienced by autos. This procedure maintains consistency in mode attributes and allows simulation of such effects as traveling through congested areas versus having an independent bus lane to be effectively represented. 1-4 ANC/TP41158.DOC/

14 SECTION 2 Travel Model Structure The Anchorage travel demand model consists of a series of processes that progressively build up forecasts of travel activity. Major inputs to these processes include the following: Land use and socio-economic data representing area development Multimodal networks representing travel options and connections between different part of the area Figure 2-1 is a flowchart illustrating the detailed sequencing and linking of the various steps used in the travel model. This section focuses on providing a broad overview of the key elements of those steps in the context of the overall process. The subsequent sections will detail assumptions, calibration and validation of each of these steps. 2.1 Land Use and Demographic Inputs Besides the networks representing multimodal transportation facilities available to prospective travelers, another primary input to the travel modeling process is an inventory or forecast of the distribution and make up of land uses, households, population and employment in the study area. In assembling inventories or developing forecasts of these inputs, attention must be paid to providing adequate detail to support the input requirements of the travel demand model. This means both (1) having estimates at the traffic analysis zone level to provide adequate geographic specificity and (2) categorizing data such as types of employment in enough detail to support input requirements. The Anchorage model design requires the following data inputs and associated breakdown for each traffic analysis zone. A detailed description of the data fields is provided in Appendix A Variable Dictionary Anchorage Travel Model Soicioecnomic Database. Traffic Analysis Zone ID Total Population Number of Households Median Household Income (2002 Dollars) Average Number of Workers/Household Enrollment of Schools in Zone Employment Categories Agricultural Employment Mining Employment Construction Employment Manufacturing Employment Transportation/Public Utilities Employment Wholesale Trade Employment Retail Trade Employment Finance, Insurance and Real Estate Employment ANC/TP41158.DOC/

15 TRAVEL MODEL STRUCTURE Service Employment Government Employment University Employment School Employment Medical/Health Services Employment Work Trip Parking Costs (2002 Dollars) Other Trip parking Cost (2002 Dollars) Categorization of employment data was based on employer reported North American Industrial Classification System (NAICS) codes to allocate the 2002 employment inventory. Future forecasts use the employment type stratification of the AMATS Land Use Allocation model directly. 2.2 Land Use/Density Inputs The new travel model provides sensitivity to environmental factors that influence tripmaking in general and, specifically, determination of mode choice. To do this, indicators of characteristics such as pedestrian friendliness, mix of land uses and similar measures are calculated based on the spatial structure of the travel model network and land use assumptions. Specifically, the values calculated in this manner are used in the auto ownership model and in all the mode choices models (specific variables used and associated coefficients are documented in Mode Choice (Section 7). These values are calculated for each traffic analysis zone. Street intersection density (per square mile); Ratio of employment to residents (per zone); Employment density (per square half mile and mile); Residential density (per square half mile and mile); 2.3 Household Segmentation Households are segmented into five different primary groupings representing their membership in the household size, number of worker, median income, auto ownership and number of children classes described in Travel Model Overview (Section 1). Depending on the model calculations being performed, each of these groups may or may not be treated separately. The trip generation models use the following household segmentations: Household size and number of workers are used together to estimate home based work productions Household size and median income class are used together to estimate both home based shopping and home based other productions Household size and number of children are used together to estimate home based school trip productions 2-2 ANC/TP41158.DOC/

16

17 TRAVEL MODEL STRUCTURE The mode choice model uses the following household segmentations: Separate models for each household size and number of workers category are used together to estimate home based work mode shares Separate models for each household size and median income range category are used together to estimate home based shopping and home based other mode shares In all cases, except the auto ownership and children per household models, the segmentation models use the zonal average value as the input and based on 2002 Census data (both SF1 tables from the general census and CTPP(Census Transportation Planning Package) tabulations), stratify that average into the segments required by the downstream models. For the auto ownership model, a multinomial logit formulation was used. For the children per household model, a cross classification formulation was used. All models are fully documented in Traveler Market (Household) Segmentation (Section 3) of this document. 2.4 Trip Distribution Separate trip distribution models were calibrated for each of the six person travel trip purposes and for the two commercial vehicle trip categories. All trip distribution formulations except home based school are based on the gravity model. Utilizing data from the Anchorage Household Travel Survey, accessibility/deterrence functions were developed for all person based trip purposes except home based school. For medium and heavy duty truck trips, the functions used in the previous Anchorage model (based on FHWA Quick Response Freight Traffic Estimation methods) were utilized. For home based school, a Fratar growth factoring model was used. This model uses a combination of a seed table of known existing origins and destinations and a parallel set of updated trip productions and attractions to grow interchanges in the seed table to match the updated production and attraction values. The process iterates to expand interchange values while using the independently determined productions and attractions for each traffic analysis zone as control totals. The output of the trip distribution model is stratified by both trip purpose and time of day. Time of day stratification is achieved by inputting the time period specific network travel times and creating three person trip tables for each of the eight trip purposes (six for person travel and two for commercial vehicle/truck travel). The process of estimating the number of trips for each trip purpose in each time period, which occurs after trip distribution is completed applies time-of-day factors and directional flow factors to the daily trip distributions to output three time-period partitioned trip tables for each trip purpose. The trip purpose specific trip distribution functions are documented in the Trip Distribution (Section 5) of this document. 2.5 Time of Day Trip estimates from the trip distribution step are used to calculate the number of trips occurring in each of three modeled time periods for all eight purposes. If congested times are used to estimate each of the time periods, origin-destination patterns for parallel (e.g. ANC/TP41158.DOC/

18 TRAVEL MODEL STRUCTURE AM peak period home based work vs. PM peak period home based work) can vary significantly. The purpose of this step in travel model processing is to produce a set of triptables which correctly represent the relative directional orientation and the proportion of trips occurring in each modeled period. This is done by multiplying each trip table by household travel survey derived factors indicating the percentage of trips from home vs. trips to home for home based trip purposes (the factor for the non-home based work purpose is based on similar survey information for that activity. Non-home based non-work is always 50/50); then, factoring the resulting table by the total percentage of trips for a given trip purpose occurring in the period. These factors and their application are fully documented in the Time of Day (Section 6) of this document. 2.6 Mode Choice The mode choice models estimate shares of all trips by mode. They comprise the most complex step in travel model processing. For the Anchorage model, mode choice is performed separately for traveler market segments, trip purpose and modeled time period. All non-commercial trip purposes are specified as multinomial logit models which estimate mode shares for all modes identified in Travel Mode Stratification in the Travel Model Overview section (Section 1). Coefficients and performance of these models is fully documented in Mode Choice (Section 7). Operationally, mode share estimates are obtained for each market segment and time period for each home based trip purpose. The segments within each time period are combined to be input to the corresponding trip assignment (either vehicle trips to highway or transit riders to transit routes). For non-home based trip purposes, mode shares are calculated in aggregate for a given time period as input to the traffic assignment process. For commercial trips (medium and heavy duty trucks), these are already estimates of vehicle trips, so no mode choice takes place. 2.7 Traffic Assignment The last step in the travel modeling process is assigning the mode specific and time period specific estimates of origin-destination interchanges to specific routes in the travel model network. The model software provides capabilities to do this for both highway and transit trips. (For transit, only public transit trips are assigned. School bus rider trips are not currently assigned to the network.) Public transit trips are assigned to the transit system network routes which are represented by stops, travel times, and waiting delays to traverse the minimum cost route between a given origin and destination via public bus or other transit mode. This assignment is sensitive to conditions in the public transit network during the assignment s time period (associated road congestion, bus frequencies by route, etc.). The model output is rider volumes by route and specific segment. This data can be examined in aggregate to determine transit riders in specific corridors or areas of the city. Highway vehicle trips are assigned using a mathematical algorithm that is sensitive to the relationship between traffic volume and available road capacity. The algorithm uses this relationship to: 1) estimate expected congestion related delays; 2) considering that delay 2-6 ANC/TP41158.DOC/

19 TRAVEL MODEL STRUCTURE recalculate the best routes between origins and destinations; 3) reassign traffic to available routes given updates to route times based on delay. This cycle theoretically (equilibrium is actually estimated using a sub-optimal heuristic that approximates an equilibrium state) continues until the condition is reached that no group of travelers can improve their origindestination travel time by choosing an alternate route. At his point, the system is said to be in equilibrium. Either the approximation of an equilibrium condition or a maximum number of iterations to reach this condition ends the assignment process. As referenced above, highway assignments in the Anchorage model are made for three specific time periods. Based on final assignment speeds, the estimated link travel times in the network are updated. These new travel times can then be reinput to the trip distribution process to show impacts of expected congestion on traveler destination and mode choices as part of the travel model feedback process. The cycle of trip distribution, time of day factoring, mode choice and traffic assignment can then be repeated until either the travel times input to the traffic assignment process and those output from the assignment process reach equilibrium of a maximum number of iterations is completed. This process uses a mathematical model termed Method of Successive Averages to achieve equilibrium and ensure closure between input speeds and volumes and those output as the final iteration. This model equilibrium feedback process is described more fully in Appendix B Model Feedback Process. The formulation used to determine overall model equilibrium is discussed in detail in Traffic Assignment and Volumes (Section 8). That section also includes discussion of the process of comparing measured to modeled traffic volumes. 2.8 Summary The purpose of this section has been to provide a roadmap to the remainder of the discussions and an overview of formulations of specific models that make up the Anchorage travel model; and, to broadly explain the process used to apply those models. The remainder of the document discusses the specific models and the measurement of their performance. ANC/TP41158.DOC/

20 TRAVEL MODEL STRUCTURE 2-8 ANC/TP41158.DOC/

21 SECTION 3 Traveler Market (Household) Segmentation This section provides detailed documentation of the models developed to segment households into different groups or market segments. The following three major data sources were utilized: 2000 US Census Transportation Planning Package 2000 US Census SF1 Short Form Statistical Tabulations 2002 Anchorage Household Travel Survey Each of the models which were developed is discussed below. 3.1 Household Size Median household size information must be translated to numbers of households falling into each size group or segment. These segments support disaggregate estimation of auto ownership, cross classification of trip production rates (for home based shop, home based school and home based other) and disaggregate estimation of mode choice (for the same trip purposes). To estimate the proportions of different sized households in each TAZ, median values from the 2000 U.S. Census data are correlated with corresponding percentages of each of the groups. This analysis was performed for both the complete census data sample (100% of the area population) at the block group level and for traffic analysis zones using the Census Transportation Planning Package subset (approximately a 6% sample of the area population). Using these two sources, curves were developed relating the proportion of one person, two person, three person and four or more person households to a range of median values. These curves are illustrated in the graphic below. The number of households in each TAZ is multiplied times these proportions to determine the number of houses in each category. For future conditions, zonal population and households estimates are used to derive mean values and the corresponding size segment for each category in each zone. Figure 3-1 illustrates the family of curves indicating the relationship between average household size and the proportion of households falling into each group for that average. ANC/TP41158.DOC/

22 TRAVELER MARKET (HOUSEHOLD) SEGMENTATION Household Size Model Group Proportion Person 2 Person 3 Person 4 Person Median Houshold Size Figure 3-1. Average Household Size vs. % Total Households by Size Group Aggregating the estimates of household size groups for the Anchorage model s study area, yields the following proportions (Table 3-1) for each group and an overall average for the study area of Comparable proportions obtained by aggregating the parallel Census (100% sample) information are also shown in Table 3-1 (average is 2.745). The household size model estimates an average household size of for households of four of more persons compared to an actual value of based on 2000 U.S. Census information. (Note that while the U.S. Census does include military housing, the estimates from the Anchorage household size model do not.) TABLE 3-1 Comparison of Household Size Model Results (2002) and 2000 Census Proportion of Household Size Group (Totals for Anchorage Area) Household Category Model Total Model Group/ Total Census Total Census Group/ Total One Person Two Person Three Person Four or More Totals ANC/TP41158.DOC/

23 TRAVELER MARKET (HOUSEHOLD) SEGMENTATION 3.2 Household Income The second model developed was similar to the household size model. This model predicts the proportion of household falling into low, medium and high household income groups based on median income for each traffic analysis zone. Median household income must be translated to numbers of households falling into specified income groups to support disaggregate estimation of auto ownership, cross classification of trip production rates (for home based shop and home based other trip purposes) and disaggregate estimation of mode choice (for home based work, home based shop and home based other trip purposes). To estimate the proportions of income groups in each TAZ, median values from the 2000 U.S. Census are correlated with corresponding percentages of the groups. This analysis was performed for both the complete census data sample (100% of the area population) at the block group level and for traffic analysis zones using the Census Transportation Planning Package subset (approximately 6% sample of the area population). Using these two sources, curves were developed relating the proportion of low income (< $40,000/year), medium income ($40,000/year to $69,999/year), and high income ($70,000/year or more) households to a range of median values. These curves are illustrated in the graphic below. Total households are multiplied times these proportions to determine the number of houses in each category. For future conditions, estimation of zonal median incomes will impact model results by changing the size of the market segment associated with each category in a zone. Figure 3-2 illustrates the family of curves indicating the relationship between the median income for a given traffic analysis zone relative to the median income in the study area (using this ratio allows all estimates to be made relative to the range of household incomes in Anchorage) and the proportion of households falling into each group for that average. Household Income Model Group Proportion Low Income Medium Income High Income Zonal/Regional Income Figure 3-2. Zonal/Regional Income vs. % Total Households by Income Group ANC/TP41158.DOC/

24 TRAVELER MARKET (HOUSEHOLD) SEGMENTATION Aggregating the estimates of household income groups for the Anchorage model s study area, yields the following proportions (Table 3-2) for each group. Comparable proportions obtained by aggregating the parallel 2000 U.S. Census (100% sample) information are also shown in Table 3-2. It should be noted that while the census does include military housing, the estimates from the household income model do not. TABLE 3-2 Comparison of Household Income Model Results (2002) and 2000 Census Proportion of Household Income Groups (Totals for Anchorage Area) Household Category Model Total Model Group/ Total Census Total Census Group/ Total Low Income (>$40000/Yr) Medium Income ($40001-$70000/Yr) High Income (>$70000/Yr) Totals As would be expected, comparisons of the household income model to 2000 U.S. Census data shows greater discrepancies then the household size model. This is likely due to the differences in the years being compared and also in the separate survey reporting categories used to report median zonal income. 3.3 Workers per Household The average number of workers per household is used to generate households falling into worker classes to support disaggregate estimation of auto ownership, estimate of home based work trip productions and estimation of home based work mode. To estimate the proportions of worker classes in each TAZ, average workers per household values from the 2000 U.S. Census are correlated with corresponding percentages of the groups. This analysis was performed for both the complete census data sample (100% of the area population) at the block group level and for traffic analysis zones using the Census Transportation Planning Package subset (approximately 6% sample of the area population). Because the CTPP dataset provides better categorization of the workers per household variable and due to logical inconsistencies in the Census (100%) sample information, curves were developed based exclusively on the CTPP data relating the proportion of zero worker, one worker, two worker, and three or more worker households to a range of average values. These curves are illustrated in Figure 3-3 below. Total households are multiplied times these proportions to determine the number of households in each category. For future conditions, estimation of the zonal average of number of workers per household will impact model results by changing the size of the market segment associated with each category in a zone. 3-4 ANC/TP41158.DOC/

25 TRAVELER MARKET (HOUSEHOLD) SEGMENTATION Group Proportion Household Worker Model Worker 1 Worker 2 Worker 3+ Worker Average Workers/Household Figure 3-3. Average Workers/Household vs. % Total Households by Group Aggregating the estimates of household worker groups for the Anchorage model s study area, yields the following proportions (Table 3-3) for each group. Comparable proportions obtained by aggregating the parallel 2000 U.S. Census information are also shown in Table 3-3. It should be noted that while the census does include military housing, the estimates from the household worker model does not. TABLE 3-3 Comparison of Household Worker Model Results (2002) and 2000 Census Proportion of Household Worker Groups (Totals for Anchorage Area) Household Category Model Total Model Group/ Total Census Total Census Group/ Total Zero Workers One Worker Two Workers Three or More Totals ANC/TP41158.DOC/

26 TRAVELER MARKET (HOUSEHOLD) SEGMENTATION 3.4 Auto Ownership The Anchorage travel model includes a model for estimating auto ownership groups. These ownership groups and the resulting estimate of autos per person per TAZ are used as an input to mode choice modeling. Vehicle (auto) ownership group shares were determined using a multinomial logit model formulation. This estimation process was chosen to allow more direct forecasting of auto ownership for future years based on primary forecasting variables. The auto ownership model forecasts group proportions for 0, 1, 2 and 3 or more auto owning households. The variables used for input include household size, number of workers in household, residential density and land use mix of the residence zone. The model is applied separately for each household income group. The logit formulation calculates segment shares based on the proportion of the total of all shares each share represents. These shares are expressed the natural exponent of an empirically derived function. The general formulation is as follows: Share(u i )=e u i/ e u t where: Share(u i ) = calculated share of some group i e u i = the natural exponent of the derived utility u i e u t = sum of all e u for all possible options The auto ownership model uses the following variables in its utility expression: Household size Number of workers per household Income segment (low, medium or high) Density of street intersections within ½ mile Index of zonal mix of residential and employment uses The following table (Table 3-4) lists the associated coefficients used in the auto ownership model: TABLE 3-4 Auto Ownership Model Variables and Coefficients Variable Zero Autos One Auto Two Autos Constant Household Size No of Workers Income Group (high) (med) (low) (high) (med) (low) (high) (med) (low) Intersection Density Mixed Use Index For households having 3 or more vehicles, the utility value is 0. This value provides the base reference for scaling the utilities of all other groups. 3-6 ANC/TP41158.DOC/

27 TRAVELER MARKET (HOUSEHOLD) SEGMENTATION Aggregating the estimates of the sizes of household auto ownership groups for the Anchorage model s study area, yields the following proportions (Table 3-5) for each group. Comparable proportions obtained by aggregating the parallel 2000 U.S. Census information are also shown in Table 3-5. It should be noted that while the census does include military housing, the estimates from the household auto ownership model do not. TABLE 3-5 Comparison of Household Auto Ownership Model Results (2002) and 2000 Census Proportion of Household Auto Ownership Groups (Totals for Anchorage Area) Household Category Model Model Group/ Total Census Census Group/ Total Zero Autos One Auto Two Autos Three or More Totals Children per Household To support estimation of Home Based School trip productions, a cross classification model was developed to correlate the number of school aged children in a household to household size and number of workers in the household. ANC/TP41158.DOC/

28 TRAVELER MARKET (HOUSEHOLD) SEGMENTATION 3-8 ANC/TP41158.DOC/

29 SECTION 4 Trip Generation 4.1 Home Based Trip Production Models Trip generation models and rates are based on information from the 2002 Anchorage Household Travel Survey. Cross-classification methods were used to estimate the number of origins (or productions) associated with each of the four home based trip purposes. These models were developed by cross classifying the trips per household by trip purpose with associated market segments. Review of the associated rates and their ability to replicate totals from the travel survey were used as criteria for both selecting the cross classification dimensions associated with each trip purpose and for determination of the number of cross classification categories. Another criterion in this process was the feasibility of developing reasonable estimation models to predict membership in the cross classification categories. Categorical rates for home base work, home based shop, home based school and home based other trip purposes are presented in the four tables (Table 4-1 through 4-4) below. TABLE 4-1 Home Based Work Trip Rates (Workers/HH by Income Group) No of Workers/HH Low Income Medium Income High Income or more TABLE 4-2 Home Based Shopping Trip Rates (Household Size by Income Group) Persons/HH Low Income Medium Income High Income or more ANC/TP41158.DOC/

30 TRIP GENERATION TABLE 4-3 Home Based School Trip Rates (Household Size by Children/HH) Persons/HH 1 Child 2 Children 3 or more Children or more TABLE 4-4 Home Based Other Trip Rates (Household Size by Income Group) Persons/HH Low Income Medium Income High Income or more These trip generation rates were applied and evaluated by comparing results obtained by their application the expanded totals from the household travel survey. These tests and an analysis of the outcomes is present in the Validation Results section at the end of Trip Generation (Section 4). 4.2 Non-Home Based Trip Production Models In addition to the four home based trip purposes, two additional trip purposes are used to describe the full range of non-commercial trips. The two purposes used for non-commercial trips are non-home based work and non-home based non-work. This distinction was applied to separate out those trips made from a work location to supplemental destinations such as midday trips to visit other businesses for work or personal reasons from other nonwork related non-home based trips. This separation of work-based and non-work based trips has been shown to improve the quality of small area forecasts of number, length and time of day of non-home based trips. The number of these non-home trips is more likely to be influenced by the characteristics of the location of their origin rather than the socio-economic characteristics of the traveler. Because of this, production models correlate these trips to inputs that describe the origin location such as the number of employees at a location. Typically, non-home based trip generation models are specified using such formulations as linear regression. In case of the 4-2 ANC/TP41158.DOC/

31 TRIP GENERATION Anchorage model, the production models for both non home-based work and non-work are specified as linear regression equations. The equations are presented below. For non-home based work, the final estimated equation was: NHBW Productions=0.809*Total Employment For non-home based non-work, the final estimated equation was: NHBNW Productions= 0.222*Population *Adjusted 1 Retail Employment *Other Employment *Number of Households 4.3 Commercial Vehicle (Truck) Trips The commercial and truck trip generation model previously developed by AMATS was reused. This model is based on a set of standard equations for trip generation developed by the Federal Highway Administration as part of the Quick Response Freight Estimation Method. The FHWA methodology specifies a set of procedures to, first, apply and, then, adjust the generic models based on available local data. These procedural steps were followed and applied as part of AMATS previous calibration process. Full documentation of this model and calibration process is provided as Appendix C Commercial Vehicle Travel Model of this report. (Note that further refinements in truck trip generation rates were formulated for Anchorage special generators). 4.4 Trip Attraction Models To complete the trip generation model set, a set of models were developed to identify where the trip productions were bound that is, the trip attraction end of each trip. As in the case of non-home based trip generation, these trip attraction models are based on the zonal characteristics of potential destination zones and are typically formulated as linear regressions. Information from the 2002 Anchorage Household Travel Survey regarding the destinations of travelers by trip purposes was tallied for each TAZ, together with the information from the land use, employment, and demographic inventories. Regression analysis was applied to this composite TAZ dataset to develop trip attraction equations for each trip purpose. These equations are presented below. Home based work attractions: HBW Attractions=0.994*Total Employment Home based shopping attractions: HBS Attractions=2.314* Adjusted 1 Retail Employment *Construction Employment Home based school attractions: HBSCH Attractions=2.091*Number of Students ANC/TP41158.DOC/

32 TRIP GENERATION Home based other attractions: HBO Attractions=0.484*Population * Adjusted 1 Retail Employment *Health Services Employment *Educational Employment *Service (inc FIRE 2 ) Employment *Construction Employment Non-home based work attractions: NHBW Attractions=2.327*Government Employment * Educational Employment * Service (inc FIRE 2 ) Employment * Industrial 3 Employment Non-home based non-work attractions: NHBNW Attractions=0.154*Population * Adjusted 1 Retail Employment *Educational Employment *Service (inc FIRE 2 ) Employment *Construction Employment 4.5 Special Generators/Visitors In addition to the trip generation based directly on zonal households and employment, the AMATS travel model incorporates the following two additional trip generation elements: 1. Estimates of trips to and from a range of special uses (e.g. airports, universities and military bases) that cannot be adequately accounted for using the standard methods described above. 2. Estimates of trips made by non-resident visitors. These two models are described below. The special generators model specifies tripmaking rates and the distribution of trips into the defined trip purposes. In most cases, all special generator trips are attractions (i.e. non-home destinations of home based or other trips being made). However, some uses also generate productions such as residential students at a college or university. The AMATS travel model design also incorporates truck trip generation for some of the special uses. Table 4-5 lists specific uses that were incorporated as special generators in the travel model. A complete listing of the associated trip rates, trip rate basis and purpose/production-attraction breakdowns is provided in Appendix D Special Generators. 4-4 ANC/TP41158.DOC/

33 TRIP GENERATION TABLE 4-5 Special Generators Elmendorf AFB-Post Rd Gate Ft Richardson Ft Richardson (Trucks only) Ship Creek (Port Area) Ship Creek (Port Trucks only) Columbia Hospital Columbia Hospital (Trucks only) University of Alaska University of Alaska (Trucks only) Alaska Pacific University Alaska Pacific University (Trucks only) Providence Medical Center Providence Medical Center (Trucks only) Loussac Public Library Alaska Native Hospital Alaska Native Hospital (Trucks only) Stevens International Airport Stevens International Airport (Trucks only) Air National Guard Base Eagle River Landfill Out of Area Trips (North) Out of Area Trips (South) Elmendorf AFB-Gov't Hill Gate Elmendorf AFB-Boniface Gate The visitor model predicts the number of trips made by visitors staying at area hotels and motels. There are three inputs to the model, as follows: An inventory of the hotels/motels; their locations in the model area; and the number of guest rooms An estimate of expected hotel occupancy based on type of property and location (currently, the travel model uses three different levels of occupancy) An estimated rate of person trips per occupied room (these trips are treated as home based other productions at the hotel/motel sites) The 2002 hotel/motel inventory and assumed occupancy for each property is provided in Appendix E Hotel/Motel Visitor Model. Based on Institute of Transportation Engineers studies and other trip generation research, the trip rate used is 8.3 trips per occupied room. ANC/TP41158.DOC/

34 TRIP GENERATION 4.6 Other Special Considerations Early results from the trip generation process indicated that significant underprediction of total trips was occurring in the vicinity of big box (e.g. Wal-Mart) stores. To correct this, these zones were identified as SPECIAL RETAIL ZONES (SRZ s) and trip rates were adjusted accordingly from national research data. Recent work in evaluating differences between trip generation for big box retail versus other retail, retail employment-related attractions for the home based shopping and home based other trip purposes was examined. This resulted in increasing home based shopping and home based other trip purposes attractions rates for SRZ s by multiplying times Validation Results After completing specification of all trip generation models, the models were scripted and then applied to the 2002 socio-economic database to test their ability to replicate existing travel. Results of the trip generation process were compared by trip purpose for the Anchorage area. The comparison is not exact because of influences of the special generator, external and visitor trips. Some of these trips are only partially or are not represented in the survey. Table 4-6 shows the comparison of survey vs. model generated total trips by trip purpose. TABLE 4-6 Total Trips by Trip Purpose (Survey-Model Comparison) Trip Purpose Survey Model Survey/Model Home based Work Home based Shop Home based School Home based Other Non-home based Work Non-home based Nonwork All Areas The conclusion that can be drawn from this is that approximately 6% of daily person tripmaking is externally or visitor based. A second comparison was made to examine the geographical distribution of trip generation. Total surveyed trips (expanded) by community council area were plotted against total modeled trips for the same areas and the correlation coefficient between the parallel data points was calculated. This plot is shown as Figure 4-1 below. 4-6 ANC/TP41158.DOC/

35 TRIP GENERATION Survey vs Model Trips Model Trips by Community Council R 2 = Survey Trips by Community Council FIGURE 4-1. SURVEY VS. MODELED TRIPS BY COMMUNITY COUNCIL AREA The results show that the trip generation and attractions models developed for Anchorage correlate well on a aggregate basis to observed data from the household survey. For reference, a R 2 (correlation coefficient) of one point zero (1.0) indicates a perfect match. Zero indicates no discernable relationship between two groups of data. ANC/TP41158.DOC/

36 TRIP GENERATION 4-8 ANC/TP41158.DOC/

37 SECTION 5 Trip Distribution Trip distribution models use the estimates of trips produced and trips attracted in an area as inputs to creating patterns of origins and their associated destinations. The Anchorage travel model utilizes two types of trip distribution models. First, a gravity model which utilizes a trip length frequency distribution from local survey data to attempt to replicate current trip patterns based on matching the associated trip length distribution for each trip purpose. Trip lengths are specified in minutes to account for delays encountered, differential speeds of different travel modes, etc. To ensure that survey vs. model trip characteristics are comparable, the starting point for this process is to use the calculated paths in the travel model network to estimate trip lengths for both survey and model trips. The second method is a growth or Fratar model. Unlike a gravity model, the Fratar model does not consider how network operating characteristics may impact travel behavior. The Fratar model starts from an existing condition table of trip interchange data and grows those interchanges based on newly input zonal totals for productions and attractions. Fratar models are typically used where either network conditions over time are expected to remain reasonably stable or where destination choice is not a function of the characteristics of the origin-destination path chosen. In the Anchorage case, Fratar distribution was used for the home based school trip purpose only. The gravity model distribution is used for all other trip purposes. The gravity model is calibrated mathematically by, given the input data, attempting to match each one minute interval (1 to 60 minutes) with the associated proportion of survey trips of that length. In the case of the Anchorage model, this produced discontinuous relationships due to the lumpiness of the survey data. The original curves were smoothed to provide a continuous function. Although the trip distribution models are executed on a time period specific basis, only one daily average curve is used for each trip purpose. For this reason, only daily trip frequency distributions are considered here. It should be noted that inputs to the trip distribution process are stratified by time period. When the model is run using feedback to the distribution process, inputs to distribution include link travel times stratified by time of day period. Distribution for each time period is calculated separately. Because the Anchorage model uses distribution feedback, the trip distribution curves had to be readjusted to consider the feedback effects. Table 5-1 shows the comparison between the survey and travel model average trip lengths by trip purpose and overall. ANC/TP41158.DOC/

38 TRIP DISTRIBUTION TABLE 5-1 Average Trip Length (in Minutes) by Trip Purpose Trip Purpose Survey Model Survey/Model Home based Work Home based Shop Home based School Home based Other Non-home based Work Non-home based Non-work All Areas Longer trip lengths for home based work trips and home based other trips in the model are largely due to the inclusion of through (trips which begin or end outside of the study area) trips in the statistics. Using the corresponding trip lengths but excluding through trips, the home based work and home based other average lengths are about 12.2 and 8.3 minutes respectively. The significantly shorter trip length for home based school trips is based on growing the ASD attendance database; not on a trip length based model. The likely cause of the difference is underreporting in the survey of the shorter, mostly non-motorized, trips. These would be trips made by children walking or riding their bikes to school whose travel was not captured in the survey. Given the incidental nature of many non-home based work trips, it s likely that differences in the survey and model average trip lengths can be attributed to a similar cause. Figures 5-1 through 5-6 detail the relationship between the surveyed trip distribution and modeled trip distribution for each trip purpose. 5-2 ANC/TP41158.DOC/

39 TRIP DISTRIBUTION HBW Daily Trip Distribution No of Trips Model Daily Survey Daily Minutes Figure 5-1. Survey vs. Model Trip Length Frequencies (Home based work) HBS Daily Trip Distribution No of Trips Model Daily Survey Daily Minutes Figure 5-2. Survey vs. Model Trip Length Frequencies (Home based shop) ANC/TP41158.DOC/

40 TRIP DISTRIBUTION HBSch Daily Trip Distribution No of Trips Model Daily Survey Daily Minutes Figure 5-3. Survey vs. Model Trip Length Frequencies (Home based school) HBO Daily Trip Distribution No of Trips Model Daily Survey Daily Minutes Figure 5-4. Survey vs. Model Trip Length Frequencies (Home based other) 5-4 ANC/TP41158.DOC/

41 TRIP DISTRIBUTION NHBW Daily Trip Distribution No of Trips Model Daily Survey Daily Minutes Figure 5-5. Survey vs. Model Trip Length Frequencies (Non-home based work) NHBNW Daily Trip Distribution No of Trips Model Daily Survey Daily Minutes Figure 5-6. Survey vs. Model Trip Length Frequencies (Non-home based non-work) Generally, the graphs show a good correspondence between the survey trip distributions and the model trip distributions. The difference in absolute magnitudes of the non-home ANC/TP41158.DOC/

42 TRIP DISTRIBUTION based non-work distribution is a reflection of the model trips which start or end outside of the study area; these trips are not included in the household survey trip set. 5-6 ANC/TP41158.DOC/

43 SECTION 6 Time of Day Factoring Time of day factoring is the straightforward process of converting the trip distribution outputs to a set of triptables that correctly represent the percent of trips in the AM, PM, and off peak periods and their directional orientation. To perform this step, these factors were developed directly from the 2002 household travel survey. The three sets of time period factors are provided in the tables below. TABLE 6-1 AM Peak Period Trip Purpose and Directional Factors Trip Purpose % Trip in Period % Home Origins % Home Destinations Home based work Home based shop Home based school Home based other Non-home based work a Non-Home based non-work Trucks a For Non-home based work trips, % Home Origins is the percent of trips with a work origin, % Home Destinations is percent of trips with a work destination. TABLE 6-2 PM Peak Period Trip Purpose and Directional Factors Trip Purpose % Trip in Period % Home Origins % Home Destinations Home based work Home based shop Home based school Home based other Non-home based work a Non-Home based non-work Trucks a For Non-home based work trips, % Home Origins is the percent of trips with a work origin, % Home Destinations is percent of trips with a work destination. ANC/TP41158.DOC/

44 TIME OF DAY FACTORING TABLE 6-3 Off Peak Period Trip Purpose and Directional Factors Trip Purpose % Trip in Period % Home Origins % Home Destinations Home based work Home based shop Home based school Home based other Non-home based work a Non-Home based non-work Trucks a For Non-home based work trips, % Home Origins is the percent of trips with a work origin, % Home Destinations is percent of trips with a work destination. After the person trip-tables output by the trip distribution model are factored into tables representing directional trips by time period, this data is input into the mode choice models to determine shares of tripmaking by specific travel modes. 6-2 ANC/TP41158.DOC/

45 SECTION 7 Mode Choice Mathematically, the mode choice models are the most complex models used in the Anchorage travel model process. Also, in order to measure competition between travel modes, they also have the largest input data requirements. Mode choice modeling in the Anchorage model is stratified in three dimensions: Trip purposes are modeled separately Within each trip purpose, time periods are modeled separately Within each time period and trip purpose, specific traveler markets are modeled separately for the home based trip purposes Mode choice models were separately defined and calibrated for each of the six person based trip purposes (not trucks). The impact of time periods is represented through supplying information regarding network conditions (e.g. road speeds, bus service frequencies) that is specific to the time period in question. Traveler market segments are represented through calibrated parameters used in the trip-purpose-specific mode choice model. Operationally, separate mode choice model runs are made for each segment (within a given trip purpose and time period) and then these results are combined to represent the time period totals. This is shown in the Model Overview flowchart (Figure 2-1). In the Anchorage model, mode choice considers the following modal options: Auto drive alone Auto drive with passenger Auto passenger Public Transit/School Bus Transit Walking Bicycling In the case of transit modes, all trip purposes except home base school estimate public transit trips. Home based school estimates school bus trips. Table 7-1 lists the variables used in the mode choice models and their description. (Note: the variables, which define market segments are defined and listed separately in the following section. ANC/TP41158.DOC/

46 MODE CHOICE TABLE 7-1 Complete List of Mode Choice Variables (All Purposes) Variable Models Using Description In-vehicle Travel Time (IVTT) Out-of--vehicle Travel Time (OVTT) Autos Available per Person (AUTOPP) No of Workers per Household (NWHH) All All HBW, HBS, HBO HBW, HBSch Time spent traveling in or on a vehicle. Typically valued lower than time spent waiting for or accessing a mode. Time spent waiting for or accessing a mode. (Walk travel time for the walk mode is not considered to be OVTT). Measured at the TAZ level as zonal auto ownership divided by zonal population Measured at the TAZ level as zonal workers divided by zonal households Total Employment within 1 mile (D_TEMP1) HBW,HBO,NHBW Measured with GIS as the sum of zonal employment within 1 mile of the center of the destination zone adjusted by the percentage of the adjacent zones within the 1 mile radius Total Employment within ½ mile (D_TEMPH) Non-retail Employment within 1/2 mile (D_NREMPH) Average Size per Household (HHSIZE) Average No of Children per Household (AVCHH) Origin Walk Node Density (O_WALKDENS) Destination Walk Node Density (D_WALKDENS) Origin Zone Retail to Total Employment Ratio (O_RERATIOH) Destination Zone Retail to Total Employment Ratio (D_RERATIOH) HBS, NHBNW HBS HBSch HBSch HBSch, NHBW, NHBNW HBSch NHBW NHBW Measured with GIS as the sum of zonal employment within ½ mile of the center of the destination zone adjusted by the percentage of the adjacent zones within the ½ mile radius Measured with GIS as the sum of zonal non-retail employment within ½mile of the center of the destination zone adjusted by the percentage of the adjacent zones within the ½ mile radius Measured at the TAZ level as zonal population divided by zonal households Measured at the TAZ level as zonal number of children divided by zonal households Measured with GIS as the sum of connections (nodes) within ½ mile of the center of the origin zone from detailed network map (not the model network) Measured with GIS as the sum of connections (nodes) within ½ mile of the center of the destination zone from detailed network map (not the model network) Measured with GIS as the sum of zonal retail employment within ½ mile of the center of the origin zone divided by corresponding total employment and adjusted by the percentage of the adjacent zones within the ½ mile radius Measured with GIS as the sum of zonal retail employment within ½ mile of the center of the destination zone divided by corresponding total employment and adjusted by the percentage of the adjacent zones within the ½ mile radius Origin Zone Parking Cost (O_PARKCOST) NHBW, NHBNW Assumed monthly parking charge for origin zone (in 2002 dollars) Destination Zone Parking Cost (D_PARKCOST) Downtown Zone (CBD) NHBW, NHBNW NHBNW Assumed monthly parking charge for destination zone (in 2002 dollars) Identifies trip either starts or ends in downtown zone 7-2 ANC/TP41158.DOC/

47 MODE CHOICE 7.1 Home Based Work Mode Choice The home based work mode choice model is run separately for four defined market segments. These include the following: Low income-one worker households Low income-two or more worker households Medium and above income- one worker households Medium and above income-two or more worker households Table 7-2 shows the list of variables and the associated coefficients used in the home based work mode choice models. Coefficients provided for the specific market groups listed above are only used in the models applied for those specific market segments (these variables and coefficients appear last and are underlined). TABLE 7-2 Home Based Work Mode Choice Model Variables and Coefficients Variable Drive Alone Drive w/ Passenger Auto Passenger Public Transit Walking Bicycling Model Constant IVTT OVTT AUTOPP NWHH Ln(D_TEMP1) Worker Household Low Income Household Home Based Shop Mode Choice The home based shop mode choice model is run separately for four defined market segments. These are: Low income-three or more person households Low income-one and two person households Medium and above income- three or more person households Medium and above income- one and two person households Table 7-3 shows the list of variables and the associated coefficients used in the home based shop mode choice models. Coefficients provided for the specific market groups listed above ANC/TP41158.DOC/

48 MODE CHOICE are only used in the models applied for those specific market segments (these variables and coefficients appear last and are underlined). TABLE 7-3 Home Based Shop Mode Choice Model Variables and Coefficients Variable Drive Alone Drive w/ Passenger Auto Passenger Public Transit Walking Bicycling Model Constant IVTT OVTT AUTOPP D_NREMPH Ln(D_TEMPH) or More Person HH Low Income Household Home Based School Mode Choice The home based school mode choice model is produces estimates of mode share for the dive alone, drive with passenger, auto passenger, walking, bicycling and school bus. This model does not estimate public transit mode share and no market segment stratification is used. Table 7-4 shows the list of variables and the associated coefficients used in the home based school mode choice models. TABLE 7-4 Home Based School Mode Choice Model Variables and Coefficients Variable Drive Alone Drive w/ Passenger Auto Passenger School Bus Walking Bicycling Model Constant IVTT Trip Mileage HHSIZE NWHH AVCHH O_WALKDENS D_WALKDENS ANC/TP41158.DOC/

49 MODE CHOICE 7.4 Home Based Other Mode Choice The home based other mode choice model is run separately for two defined market segments. These are: Three or more person households One and two person households Table 7-5 shows the list of variables and the associated coefficients used in the home based other mode choice models. Coefficients provided for the specific market groups listed above are only used in the models applied for those specific market segments (these variables and coefficients appear last and are underlined). TABLE 7-5 Home Based Other Mode Choice Model Variables and Coefficients Variable Drive Alone Drive w/ Passenger Auto Passenger Public Transit Walking Bicycling Model Constant IVTT OVTT AUTOPP D_TEMP or More Person HH Non-Home Based Trip Mode Choice The non-home based trip mode choice models is produces estimates of mode share for trips with one end at work and the other end at some location other than the traveler s home and where the trip both originate at and is destined to a non home-non work location. The mode choice models for non-home based trips rely on knowledge of the characteristics of the origin and destination locations, not of the characteristics of the traveler. The tables below shows the list of variables and the associated coefficients used in the two non-home based trip mode choice models. ANC/TP41158.DOC/

50 MODE CHOICE TABLE 7-6 Non-Home Based Work Mode Choice Model Variables and Coefficients Variable Drive Alone Drive w/ Passenger Auto Passenger Public Transit Walking Bicycling Model Constant IVTT OVTT O_WALKDENS O_RERATIO D_RERATIO Ln(D_TEMP1) O_PARKCOST D_PARKCOST TABLE 7-7 Non-Home Based Non-work Mode Choice Model Variables and Coefficients Variable Drive Alone Drive w/ Passenger Auto Passenger Public Transit Walking Bicycling Model Constant IVTT OVTT O_WALKDENS CBD Ln(D_TEMPH) O_PARKCOST D_PARKCOST Validation Results Following the calibration and final adjustment of the mode choice models, the models were applied to forecast 2002 mode choice using the 2002 socio-economic database, travel networks and related assumptions. These forecasts were compared to the mode shares from the household travel survey. The survey data for public transit was also checked and adjusted on the basis of transit rider counts from 2003 to improve the allocation of trips by time of day. Table 7-8 through 7-14 presents the results of this comparison. Information 7-6 ANC/TP41158.DOC/

51 MODE CHOICE included is number of survey vs. model trips by mode by trip purpose and for the system as a whole. Each table presents the following for each trip purpose: Survey and model trip totals Ratio between the two totals Absolute difference between the two totals Resulting difference of estimated mode share TABLE 7-8 Survey vs. Model Mode Shares for Home Based Work Trips Mode Survey Model Ratio Diff Share Diff DA DP AP TR WALK BIKE AUTO TOTAL TABLE 7-9 Survey vs. Model Mode Shares for Home Based Shop Trips Mode Survey Model Ratio Diff Share Diff DA DP AP TR WALK BIKE AUTO TOTAL ANC/TP41158.DOC/

52 MODE CHOICE TABLE 7-10 Survey vs. Model Mode Shares for Home Based School Trips Mode Survey Model Ratio Diff Share Diff DA DP AP SB WALK BIKE AUTO TOTAL TABLE 7-11 Survey vs. Model Mode Shares for Home Based Other Trips Mode Survey Model Ratio Diff Share Diff DA DP AP TR WALK BIKE AUTO TOTAL Note: Model Totals include Hotel Visitor Model Trips 7-8 ANC/TP41158.DOC/

53 MODE CHOICE TABLE 7-12 Survey vs. Model Mode Shares for Non-Home Based Work Trips Mode Survey Model Ratio Diff Share Diff DA DP AP TR WALK BIKE AUTO TOTAL TABLE 7-13 Survey vs. Model Mode Shares for Non-Home Based Non-work Trips Mode Survey Model Ratio Diff Share Diff DA DP AP TR WALK BIKE AUTO TOTAL ANC/TP41158.DOC/

54 MODE CHOICE TABLE 7-14 Survey vs. Model Mode Shares for All Purposes Mode Survey Model Ratio Diff Share Diff DA DP PA TR SB WALK BIKE AUTO TOTAL Examining the predicted vs. observed mode shares indicates that given similar assumptions regarding socio-economic conditions and networks, the model has the ability to reproduce the observed mode choice behavior ANC/TP41158.DOC/

55 SECTION 8 Traffic Assignment and Volumes One of the most important indicators of model performance is the direct measurement of how well the travel model, in an attempt to replicate base (in this case, 2002) conditions can mimic existing traffic conditions as indicated by field counts of traffic volumes throughout the network. Such model validation is industry standard practice and a necessary prerequisite to using the model to forecast expected futures. To guide travel modelers in establishing minimum standards for the travel model development process, the Federal Highway Administration has published a set of guidelines for acceptable performance. To validate the Anchorage model, four different types of model performance were measured. These included the following: Measured versus modeled aggregate traffic counts for selected cordons or cutline crossings Percent difference in volume between measured and modeled stratified by facility type Correlation measures between measured and modeled volumes for all available count locations Comparison of measured and modeled volumes at individual locations. The count data used for comparison was all day weekday traffic counts for Sources were both ADOT and MOA counts supplied for this purpose and the ADOT Volumes map for the Anchorage and the Eagle River areas. These observed volumes were compared to the sum of the AM, PM and off peak period outputs for the 2002 Base travel model alternative utilizing trip distribution feedback. The results of each of these comparisons along with the corresponding Federal guidelines are presented below. 8.1 Cordon Crossings Measurement of traffic across cordons or screenlines provides an indication of how well the model performs in replicating major trip patterns and movements throughout the network. Cordons typically encompass all facilities that serve the same definable travel corridor. This allows for the fact that the model may not perfectly represent competition between parallel facilities. Federal guidelines for acceptable performance are based on a sliding scale which ranges from acceptable deviations of 65% for 5000 vehicles to 15% for 200,000 of more vehicles (Figure 8-1). ANC/TP41158.DOC/

56 TRAFFIC ASSIGNMENT AND VOLUMES Deviation Volume(1000's) Figure 8-1. Maximum Desirable Deviation in Total Screenline Volumes For evaluating the Anchorage model s performance, 23 separate cordons were defined. These ranged from specific east-west and north-south crossings in the downtown area to longer cordons which, in some cases, bisect the entire Anchorage Bowl. The cordons used and their associated identifiers are shown in Figure 8-2. Once the cordons were defined, special procedures were written to automatically generate inputs to a set of spreadsheets to report their performance both in tabular and graphic form. The resulting spreadsheet reports which list and display counted and modeled traffic volumes for all links intersected by each cordon are provide as Appendix F Screenlines. Table 8-1 itemizes performance of each cordon along with the measured deviation and the corresponding maximum deviation based on the Federal guideline. (Diff equals actual difference Max Diff equals maximum desirable difference based on Federal guideline). Based on Table 8-1, all cordons meet or exceed Federal guidelines. Of the 23, only six cordon have deviations greater than 0.10 and no screenlines have deviations greater than Also, there does not seem to be any discernable pattern linking the cordons with a greater than 0.10 deviation. They are distributed throughout the study area. This is an indicator that there is no systematic bias in the model process. Examination of graphs showing the relative facility counts and volumes for each cordon (Appendix F Screenlines), shows that, in almost all cases, the relative magnitude of traffic volumes on specific facilities in a given corridor (i.e. for a given cordon) correlates well with observed count data. 8-2 ANC/TP41158.DOC/

57 TRAFFIC ASSIGNMENT AND VOLUMES Figure 8-1 ANC/TP41158.DOC/

58 TRAFFIC ASSIGNMENT AND VOLUMES TABLE 8-1 Summary of 2002 Weekday Cordon Counts/Volumes and Differences ID Cordon Count Volume Diff Max Diff 101 N of Tudor Rd Minnesota to Muldoon N of Dimond Av Minnesota to Birch S of O'Malley C St to Hillside S of Glenn Hwy Ingra to Muldoon W of Muldoon Tudor Rd to Glenn Hwy W of Boniface Tudor Rd to Davis W of Birch Rabbit Creek to Abbott E of Lake Otis Tudor Rd to Commercial Rd E of Lake Otis De Armoun to Dowling E of New Seward Rabbit Creek to 3rd S of Int'l Airport Minnesota to Lake Otis S of Dimond Minnesota to Birch N of Eagle River Glenn Hwy N of Eagle River Rd Access Glenn to Birchwood S of Hiland Glenn Hwy N of 3rd St C St to Port Access W of Gambell -3rd St to 16th S of 9th L St to Medfra E of Ingra 3rd to 15th N of Fireweed/Northern Lights Minnesota to M ldoon E of Northwood Northern Lights to Int'l Airport N of Int'l Airport Spenard to Lake Otis W of Minnesota Raspberry to Klatt S End of Study Area Seward Hwy Combined Cordon Totals Note: Differences >0.10 are highlighted 8.2 Facility Type Comparisons The second validation test performed on the Anchorage model was to compare the percentage of measured versus modeled traffic for different facility types. This measures how well assumed and calculated speeds in the model are distributing traffic between the different types of facilities present in the model. As with cordons, FHWA has developed guidelines to measure what is considered to be acceptable performance for a travel model. 8-4 ANC/TP41158.DOC/

59 TRAFFIC ASSIGNMENT AND VOLUMES The facility types specified for comparison in the Federal guidelines are Freeways, Major Arterials, Minor Arterials and Collectors. To create a basis for comparison with the defined facility classes, the Anchorage model designations were grouped into these four categories using the following rules. Freeways were designed as Freeways (CLASS=1); Expressways and major arterials were designated as Major Arterials (CLASS=2,3); Minor arterials and frontage roads were designated as Minor Arterials (CLASS=4); Collectors and local roads were designated as Collectors (CLASS=5,6). Counts and volumes for each category were summarized and then the aggregate difference was compared to the corresponding Federal guideline. This summary is presented below in Table 8-2. TABLE 8-2 Summary of 2002 Weekday Facility Class Counts/Volumes and Differences Facility Class Count Volume Diff Max Diff Freeway Major Arterial Minor Arterial Collector All Classes As the table indicates, measured and modeled volumes for freeways and major arterials are well within the identified targets. For minor arterials and collectors the model is slightly underpredicting but is within 5% of the targets in both cases. Particularly for the most significant facilities, this indicates good correspondence with traffic count information. This conclusion is reinforced by the results of statistical analysis presented below. 8.3 Comparisons of Statistical Performance While both the cordon and facility class comparisons use a subset of the count information, the third performance evaluation approach used measures the overall statistical performance of the model by comparing all available counts to corresponding model volumes. Figure 8-3 is a scattergram, which shows all measured and modeled volumes pairs. Fitting a linear function to this dataset yields an R 2 or correlation coefficient of.9043, which exceeds the Federal guideline of.880. The second statistical measure used is rootmean-square-error (RMSE). The RMSE for the same dataset is This RMSE value is comparable with findings for other regional travel models (no Federal guidelines are proscribed). ANC/TP41158.DOC/

60 TRAFFIC ASSIGNMENT AND VOLUMES Counts/Volumes Travel Model Volumes R 2 = Daily Traffic Counts Figure 8-3. STATISTICAL COMPARISON OF ALL AVAILABLE MEASURED AND MODELED 2002 WEEKDAY TRAFFIC VOLUMES 8.4 Individual Link Performance Federal guidelines are also provided for percent difference targets between measured and modeled traffic volumes for individual links based on total link volume. These targets are listed in Table 8-3. TABLE 8-3 Percent Difference Targets for Daily Volumes for Individual Links Average Annual Daily Traffic Maximum Desirable Deviation <1, ,000-2, ,500-5, ,000-10, ,000-25, ,000-50, >50, ANC/TP41158.DOC/

61 TRAFFIC ASSIGNMENT AND VOLUMES Using these guidelines, all count locations specific performance was evaluated. Of the 655 individual counts, 233 exceed the Federal targets. However, when corrections are made for directional imbalances in the model due to count information being non-directional, this number is reduced to 80. When only those locations with a daily count of greater than 2500 are considered, the number of locations exceeding Federal targets is 53 or about 8.1% of all count locations. In general, this indicates that the Anchorage model is replicating existing traffic behavior not only at the systemwide, facility group, and cordon levels, but also at the level of the individual road segment. ANC/TP41158.DOC/

62 TRAFFIC ASSIGNMENT AND VOLUMES 8-8 ANC/TP41158.DOC/

63 SECTION 9 Summary and Conclusions A new and more robust travel demand model has been calibrated and validated for the Anchorage metropolitan area. This memorandum documents the new model structure, inputs, calibration and validation. This new Anchorage travel demand model incorporates up-to-date travel behavior relationships for Anchorage residents (2002 Household Travel Survey) and the latest available demographic and employment attributes (2000 U. S. Census and 2002 Alaska Department of Labor employment database) for the metropolitan area. The new model structure is significantly more robust and sophisticated than the travel model previously used in Anchorage. It incorporates dynamic relationships and sensitivity to land use development densities and patterns, household composition and demographic characteristics, transportation system connectivity, cost and level of service performance, and traveler decision-making regarding mode choice selection from available options. In addition to traditional travel model functionality and features, the new Anchorage travel model interfaces seamlessly with the Anchorage Land Use Allocation Model. It also now incorporates a rich and powerful set of over two dozen performance evaluation capabilities, display outputs and metrics. These evaluation tools are entirely new -- and critically important to assist in assessing alternative transportation scenarios and related transportation system planning, operations, and design analysis. Additionally, the entire model tool set is integrated in a user-friendly, menu-driven software environment which is far easier to use and maintain. Technical documentation is internally embedded for each model software application step. The details of the model calibration are documented in this report. The new travel model validation is complete and demonstrated to be in compliance with Federal Highway Administration federal guidelines. Socio-economic and demographic attributes of households are consistent with 2000 U.S. Census and State/local data Model-estimated person trip generation matches the 2002 Anchorage Household Travel Survey results Model-estimated transit passenger trips are within 1 percent of People Mover s reported bus riders for 2002 Model-estimated vehicle volumes compared to observed traffic counts for each of 23 different cordons across road links are each within FHWA guideline criteria. For all cordons combined, actual vs. model-estimated traffic volumes are within 5.5% ANC/TP41158.DOC/

64 SUMMARY AND CONCLUSIONS A statistical linear regression fit of model-estimated vehicle volumes compared to all available observed traffic counts throughout the Anchorage road network shows a R 2 correlation coefficient of , again bettering the FHWA guideline criteria of In sum, the Anchorage Metropolitan Area Transportation Solutions (AMATS) organization now has an up-to-date, context-sensitive, powerful, proven and fully integrated travel demand modeling and analysis toolkit to address transportation planning and analysis needs. 9-2 ANC/TP41158.DOC/

65 Appendix A Variable Dictionary Anchorage Travel Model Socioeconomic Database ANC/TP41158.DOC/

66 APPENDIX A Variable Dictionary Anchorage Travel Model Socioeconomic Database (Note: Does not include computed values) Traffic Analysis Zone ID ZONE [integer] Total Population - POP [integer] Number of Households TOTALHH [integer] Median Household Income (2002 Dollars) AVINC [integer] Average Number of Workers/Household AVWHH [real (X.XXX)] Enrollment of Schools in Zone SENROLL [integer] EMPLOYMENT CATEGORIES o o o o o o o o o o o o o Agricultural Employment AEMP [integer] Mining Employment BEMP [integer] Construction Employment CEMP [integer] Manufacturing Employment DEMP [integer] Transportation/Public Utilities Employment EEMP [integer] Wholesale Trade Employment FEMP [integer] Retail Trade Employment GEMP [integer] Finance, Insurance and Real Estate Employment HEMP [integer] Service Employment IEMP [integer] Government Employment JEMP [integer] University Employment UEMP [integer] School Employment SEMP [integer] Medical/Health Services Employment XEMP [integer] Work Trip Parking Costs (2002 Dollars-Daily) - WPKCOST [real (X.XX)] Other Trip parking Cost (2002 Dollars-Monthly) - WPKCOST [real (X.XX)] ANC/TP41158.DOC/ A-1

67 VARIABLE DICTIONARY ANCHORAGE TRAVEL MODEL SOCIOECONOMIC DATABASE ANC/TP41158.DOC/ A-2

68 Appendix B Model Feedback Process ANC/TP41158.DOC/

69 APPENDIX B Model Feedback Process Feedback Using the Method of Successive Averages (Source: TransCAD V4.7 Travel Demand Forecasting) In [direct feedback], congested travel times taken from the trip assignment results are directly fed back into the highway skim procedure. This method is commonly called the Direct Feedback Method. While Direct Feedback is relatively easy to understand and implement, many modelers who have implemented this method have reported a requirement of many feedback iterations before convergence occurs, or worse, the failure to reach convergence. Theoretically, there is no guarantee of convergence with this method (TMIP: Incorporating Feedback in Travel Forecasting, March, 1996). Because of this problem, alternatives to the Direct Feedback Method have been developed. One method that has had success is the Method of Successive Averages (MSA). In the MSA method, output volumes from trip assignment from previous iterations are weighted together to produce the current iteration s link volumes. Adjusted congested times are then calculated based on the normal volume-delay relationship. This adjusted congested time is then fed back to the skimming procedures. The adjusted volume is calculated based on the following equation: ANC/TP41158.DOC/ B-1

70 MODEL FEEDBACK PROCESS ANC/TP41158.DOC/ B-2

71 Appendix C Commercial Vehicle Travel Model ANC/TP41158.DOC/

72 APPENDIX C Commercial Vehicle Travel Model This section documents the estimation of base year commercial vehicle (or truck) models to support the update of the Anchorage Transportation demand Model. Truck models were developed using local socioeconomic (employment and population) data; local truck classification data recently collected by both the Municipality of Anchorage (MOA) and Alaska Department of Transportation (ADOT); and techniques outlined in the Quick Response Freight Manual (QFRM). The QFRM was developed by Cambridge Systematics for the Federal Highway Administration (FHWA) as a sketch planning tool for Metropolitan Planning Organizations (MPOs) and state departments of Transportation (State DOTs) to implement cost-effective truck activity models. The Anchorage truck models represent base year daily (24-hour) conditions of single unit and combination commercial vehicle traffic. The truck vehicle categories correspond to those specified in the QFRM and also correspond to the standard vehicle classifications as specified by FHWA. These include: Single unit trucks FHWA Classes 5 to 7; and Combination trucks FHWA Classes 8 to 13. The following sections present the data used to support truck model development, the model development and application procedures used to both estimate and apply truck models for Anchorage, and the outputs generated from the truck modeling process. The model development and application section presents the methods used to develop truck trip generation, distribution, and assignment models, as well as the procedures used to validate the models. 1.1 Data Inputs The data inputs used to develop truck activity models for the Anchorage metropolitan area included employment data, transportation networks, and truck classification data. Socioeconomic Dataset Socioeconomic data, primarily employment data, were obtained from the MOA to represent base year conditions by transportation analysis zone (TAZ) contained in the transportation network. Data were aggregated to be consistent with QRFM techniques. Employment data were aggregated into four categories: 1) Agriculture, Mining, and Construction; 2) Manufacturing, Transportation, Communications, Utilities, and Wholesale Trade; 3) Retail Trade; and 4) Office and Services. Household data were also used to represent household activity by TAZ. ANC/TP41158.DOC/

73 COMMERCIAL VEHICLE TRAVEL MODEL Transportation Network The updated highway network from the Anchorage Transportation Demand Model was refined to incorporate roadway restrictions of heavy-duty truck movements on specific segments of Northern Lights Boulevard and E Street. This revised network was used to obtain zone-to-zone travel times and impedances to apply truck trip distribution (gravity) models. Vehicle Classifications Truck classification data obtained from the MOA and ADOT were incorporated into the highway networks and were used for truck model validation. Classification data were obtained from the Highway Performance Monitoring System (HPMS) dataset. HPMS data was supplemented by classification data from the MOA and ADOT. Vehicle counts and classification data were also obtained from the Port of Anchorage and the Anchorage Landfill to specifically model special truck generators. 1.2 Model Development and Application Results This section presents the model development, application, and validation procedures used to estimate truck models for the Anchorage metropolitan area. Truck Trip Generation Models The QFRM procedures were used to perform truck trip generation. The data processing and modeling steps for truck trip generation are presented below. Internal TAZs Truck trip generation rates from the QFRM, as shown in Table 1.1, were applied to employment and households in each internal TAZ. Employment by category and household were taken from two Excel files developed by the MOA: EMPLOY.XLS and POPTAZ.XLS. According to a note in the employment file, military employment was not included. However, there did seem to be some employment allocated to the military base zone, which is, perhaps, civilian employment on the base. Also note that there were a number of zones with neither employment nor households. Accordingly, these internal zones did not generate any truck trips. ANC/TP41158.DOC/ C-2

74 Table 1.1 Trip Generation Rates Applied to Internal Zones Generator Employment: Agriculture, Mining and Construction Manufacturing, Transportation, Communications, Utilities and Wholesale Trade Commercial Vehicle Trip Destinations (or Origins) per Day QFRM Rates Adjusted Rates Single Unit (6+ Tires) Combination Single Unit (6+ Tires) Combination Retail Trade Office and Services Households Source: Table 4.1, page 4-4, Quick Response Freight Manual (Cambridge Systematics, 1996). Trip generation rates were increased by 24 and 32 percent for single unit and combination vehicles, respectively, to bring the total screenline volumes closer to the validation data. External TAZs As recommended by the QFRM, vehicle classification count data was used to estimate truck productions and attractions at the two external stations. Truck counts on the links closest to the external stations in the validation database were used for this purpose, ensuring a good fit of trips generated by the model to the observed data. Special Generators Based on information provided by the MOA, the Port of Anchorage and the Anchorage Landfill were treated as special generators based on high levels of truck activity compared to the average transportation analysis zone within the Anchorage metropolitan area. Port of Anchorage (TAZ 13) For the Port TAZ, the number of total truck trips generated using the QFRM technique, including light duty, single unit, and combination vehicles, was comparable to the number of combination vehicle trips cited in the Traffic Flow Study conducted for the Port of Anchorage in 1996 (see Table 1.2 below). Since the Port s Traffic Flow Study figures are corroborated by information gathered during the recently-conducted carrier interviews, the combination vehicle trip productions and attractions were increased, resulting in the following trips generated by vehicle category: ANC/TP41158.DOC/

75 COMMERCIAL VEHICLE TRAVEL MODEL Table 1.2 Comparison of Combination Vehicle Traffic at the Port Port Tenant From Port Study From Interviews MAPCO to 40 QFRM Trip Generation Tesoro 48 Texaco 55 SeaLand to 200 TOTE TOTAL 573 Combination: 39 Total Commercial Vehicle: 571 Note: Port study and interviews represent Tuesday, a high volume day for Port traffic. Landfill (TAZ 648) For the landfill, information provided by the MOA, including the total number of trips by vehicle category for 1997 and monthly volumes, was used to generate the following productions and attractions: Single unit 75 trips (these represent mostly garbage trucks); and Combination 41 trips (these represent tractor-trailer combinations). Trips were scaled by the total volume for the month of September to represent an average annual condition. Total Trips The total trips generated by vehicle category for the Anchorage metropolitan area are shown in Table 1.3. TransCAD Files The truck trip generation data is stored in the geographic layer TRIPGEN.DBD. Productions and attractions are stored in the fields P_SINGLE, A_SINGLE, P_COMBI, and A_COMBI. The scaled up versions of the productions and attractions are stored in fields with the prefixes P2 and A2. Productions and attractions were balanced and written to the binary data tables BALANCED.BIN and BALANCD_2.BIN Trip Distribution Models The QFRM procedures were also used to perform truck trip distribution as presented below. ANC/TP41158.DOC/ C-4

76 Table 1.3 Summary of Households, Employment, and Commercial Vehicle Trips Variable Count (TAZs with value) Sum RETAIL ,188 FIRESVCS ,317 MANUFWTCU ,214 RESOURCES ,819 TOTALEMP ,538 SFHH ,366 MFHH ,782 POP ,853 TOTHH ,148 Single Unit Productions ,388 Single Unit Attractions ,359 Combination Productions ,073 Combination Attractions ,080 Note: Trips represent productions and attractions scaled up from QFRM. Gravity Models The trip distribution models were estimated using gravity models with an exponential specification and with coefficients by vehicle category as recommended by the QFRM. The models take the following form: ( c* tij ) F ij = e where: F is the friction factor; t is the travel time between Zones i and j; and c is a coefficient. The coefficients recommended by the QFRM are 0.1 for single unit vehicles and 0.03 for combination vehicles. These coefficients were used when distributing internal trips. However, a modified coefficient was applied when distributing trips from the external stations, as explained under the validation and calibration section. Transportation Networks and Travel Times The highway network used to generate zone-to-zone travel times was basically the same as for passenger travel modeling with one exception: several links along Northern Lights Boulevard ANC/TP41158.DOC/

77 COMMERCIAL VEHICLE TRAVEL MODEL and on E Street north of 3 rd were disabled for combination vehicles. This adjustment was taken to simulate the more circuitous path that heavy-duty vehicles are likely to take when traveling to and from the airport or port. Separate travel time matrices for single unit and combination vehicles were applied in conjunction with the coefficients described above to produce the truck friction factor matrices for each vehicle class. TransCAD Files The network file used in producing travel times was SS_STRTS.NET. A selection set (NO_TRKS) in the map (TRUCKS.MAP) was used to disable links, when necessary, using the network update feature. The friction factors are contained in a matrix file called TRKFFACT.MTX with matrices SINGLE and COMBI. The final truck trip table is stored in TRUCKS_3.MTX Truck Trip Assignment Models The truck trip tables were assigned using an all-or-nothing assignment method contained in TransCAD and the SS_STRTS.NET network file. Again, specific links on Northern Lights Boulevard and E Street were disabled when assigning combination vehicle traffic. Link-level outputs from the final model run were stored in two binary data files: SINGLE_3.BIN and COMBI_3.BIN. Total VMT without centroids was 225,334 and 113,513 for single unit and combination traffic, respectively Truck Model Validation and Calibration This section presents the process used to validate truck models for the Anchorage metropolitan area. As stated previously under truck trip generation, trip generation rates were increased by 24 and 32 percent for single unit and combination trucks to better match observed data. Validation Database The database of vehicle classification counts was developed from several different sources, the primary source being the HPMS database. This source was expanded and modified in the following manner: The percent single unit and combination traffic in the HPMS data were combined with the 1994 AADT and split into two-way traffic to come up with truck counts by direction on each link that had HPMS classification data; Where new classification count data were available (those locations counted in September 1998), the new truck percents were scaled by the 1994 AADT on the link to come up with validation counts; and In some cases, where 1994 data were not available, AADT from adjacent links or AADT from the next most recent year were applied to come up with the validation counts. These steps produced the most consistent and complete validation database possible with the available data. ANC/TP41158.DOC/ C-6

78 Validation Procedures Because validation data were not available for many links, it was not possible to successfully use the built-in TransCAD screenline analysis procedures. Rather, the screenlines were drawn on a map as graphic representations and then a selection set of links with validation data that crossed the screenlines was developed for comparison of counts to assigned flows. The screenline locations are shown in Figure 1.1 and include: 1. Glenn Highway near northernmost external station; 2. Glenn Highway approaching Anchorage proper; 3. Port of Anchorage (Ocean Dock Road); 4. North-south between International Airport and Raspberry; 5. North-south between O Malley and Huffman; 6. Seward Highway near the southernmost external station; 7. East-west airport approaches; 8. East-west, west of Seward Highway; 9. East-west, east of Boniface; and 10. East-west, west of Birch, Abbott, and Rabbit Creek. Summary of Calibration Three separate model runs were completed before the model was judged to perform satisfactorily. The results and adjustments made are described below. Baseline 1 The initial run revealed a problem with trip distribution in that almost all the trips generated at the external stations were remaining intra-taz trips. This occurred because the external zones are connected to the network with extraordinarily long connectors (almost 40 miles for Zone 901) and trips were simply not being distributed out of the zone with the QFRM gravity models. Some experimentation with the exponential function was made to select a coefficient that would result in a flatter curve, resulting in lower friction factors over the 30- to 40-mile distance range. This coefficient, 0.015, was applied only when distributing trips to or from the external stations in subsequent model runs. The other adjustment made at this stage was that the trip generation from the Port of Anchorage TAZ was revised to match the link counts on Ocean Dock Road. These were the only problems diagnosed at this stage and other aspects of the model performance, such as overall screenline performance, were not examined at this time. Baseline 2 An analysis of links crossing the screenlines revealed that the total assigned volumes were approximately 60 percent of the link counts by approximately 24 and 32 percent for single unit and combination vehicles, respectively (see attached Table 1.4). In addition, the link volumes crossing Screenline 1 were quite low. Two adjustments were made to the trip generation models for the next model run: ANC/TP41158.DOC/

79 COMMERCIAL VEHICLE TRAVEL MODEL The HPMS-based counts, rather than the previously-used ADOT classification count were used for trip generation at TAZ 900; and The QFRM trip generation rates were increased by 24 and 32 percent to bring up the total link volumes. Baseline 3 After making the above-described adjustments, another model run and screenline analysis were made. As shown in the attached Table 1.5, the total assigned volume fit was much closer to the observed volumes. The combination vehicle volume was 4 percent higher compared to observed data, while the single-unit traffic was low by 12 percent. The combination vehicle traffic was higher than expected because of the change in traffic generated at the Port TAZ between Baseline 2 and Baseline 3. In Baseline 2, the Port trip generation had been matched to the counts on Ocean Dock Road. In Baseline 3, the trip generation from the Port was inadvertently left at the higher levels developed through the special generator analyses. Since the level of traffic generated at the Port is subject to judgement, these results were left in place for the time being. TransCAD Files A selection set of the links used in the screenline analysis has been saved in TRUCKS.MAP. 1.3 Outputs Daily truck trips, trip tables by single and combination units, and a combined truck network assignment were validated for the truck model. These data will be used as inputs into the timeof-day and trip assignment elements of the base year Anchorage Transportation Demand Model. The procedures presented above will also form the foundation for developing future forecasts of truck travel in the Anchorage metropolitan region. ANC/TP41158.DOC/ C-8

80 Table 1.4 Screenline Validation Results Baseline 2 Count Data Link Flows Link Flow/Link Count Ratio Single Unit Combination Single Unit Combination Single Unit Combination Link ID FEANME AB_H_S BA_H_S AB_H_C BA_H_C AB_FLOW BA_FLOW AB_FLOW BA_FLOW AB BA Total AB BA Total GLENN NEWGLENN ,203 1, OCEANDOCK BRAYTON C MINNESOTA 1,120 1, NEWSEWARD 2,012 2,012 1,006 1,006 1,788 1, ,273 4,273 1,300 1,300 3,192 3,019 1,397 1, BIRCH LAKEOTIS C SOUTHPORT NEWSEWARD DIMOND RASPBERRY INTERNATIONAL AIRPORT OLDINTERNATIONAL AIRPORT WHITNEY TH NORTHERNLIGHTS TUDOR 3,208 3,208 2,085 2, , TH TH ,015 4,015 2,366 2,366 1,052 1, COMMERCIAL PENLAND DEBARR TUDOR 1,662 1,662 1,662 1,662 1,383 1, ,012 3,139 1,964 1,942 2,325 2, , O MALLEY HUFFMAN ,105 15,319 6,639 6,807 9,193 9,726 4,431 4, ANC/TP41158.DOC/ C-9

81 COMMERCIAL VEHICLE TRAVEL MODEL Table 1.5 Screenline Validation Results Baseline 3 Count Data Link Flows Link Flow/Link Count Ratio Single Unit Combination Single Unit Combination Single Unit Combination SC Link ID FEANME AB_H_S BA_H_S AB_H_C BA_H_C AB_FLOW BA_FLOW AB_FLOW BA_FLOW AB BA Total AB BA Total GLENN NEWGLENN ,775 1,773 1,100 1, OCEANDOCK C BRAYTON NEWSEWARD 2,012 2,012 1,006 1,006 2,231 2,214 1,084 1, MINNESOTA 1,120 1, ,143 1, SOUTHPORT C LAKEOTIS BIRCH NEWSEWARD RASPBERRY OLDINTERNATIONAL AIRPORT DIMOND INTERNATIONALAIR PORT TH TH TUDOR 3,208 3,208 2,085 2,085 1,357 1, NORTHERNLIGHTS , TH WHITNEY DEBARR PENLAND COMMERCIAL TUDOR 1,662 1,662 1,662 1,662 1,954 2,080 1,064 1, HUFFMAN O MALLEY ,105 15,319 6,639 6,807 12,509 14,128 6,733 7, ANC/TP41158.DOC/ C-10

82 Appendix D Special Generators ANC/TP41158.DOC/

83 APPENDIX D Special Generators TAZ PRATE PID ARATE AID HBWP HBSP HBSCHP HBOP NHBWP NHBNWP SINGLEP COMBIP HBWA HBSA HBSCHA HBOA NHBWA NHBNWA SINGLEA COMBIA LOCID Base Emp Base Emp Elmendorf-Post Rd Base Pop Base Emp Ft Richardson Base Emp Base Emp Ft Richardson - Truc Emp Emp Ship Creek (Trucks) Freight Tons (M) Frieght Tons (M) Ship Creek - Port Beds Beds Columbia Hospital Emp Emp Columbia Hos - Truck Students Students UAA Emp Emp UAA-Trucks Students Students APU Emp Emp APU-Trucks Students Students UAA Emp Emp UAA-Trucks Out Patients OutPatients Providence Medical Emp Emp Provid Hosp-Trucks Emp Emp Library 288 None Emp Alaska Native Hospit Emp Emp Al Native Hosp - Trk Enplanements Enplanements Airport Landings Landings Airport-Trucks 343 None Unit Air National Guard Region Pop(100s) Region Pop(100s) Eagle River Site Ext Pop(100s) Ext Pop(100s) North External Region Emp(100s) Region Pop(100s) South External Base Pop Base Emp Elmendorf-Gov't Hill Base Pop Base Emp Elemendorf-Boniface ANC/TP41158.DOC/ F-25

84 Appendix E Hotel/Motel Visitor Model ANC/TP41158.DOC/

85 APPENDIX E Hotel/Motel Visitor Model ZONE ROOMS OCC NAME ADDRESS Ramada Limited Hotel of A 207 MULDOON RD Comfort Inn 111 W SHIP CREEK AVE Anchorage Uptown Suites 234 E SECOND AVE Anchorage Grand Hotel 505 W SECOND AVE Hilton Anchorage Hotel 500 WEST THIRD AVE Historic Anchorage Hotel 330 E ST Ramada Inn Anchorage Dowt 115 E THIRD AVE Merrill Field Inn 420 SITKA ST Econo Lodge 642 E FIFTH AVE The Hotel Captain Cook FOURTH AVENUE AT K STREET Copper Whale Inn 440 L ST Holiday Inn Downtown 239 W FOUTH AVE Anchor Arms Hotel 433 EAGLE ST Rodeway Inn 1124 E 5TH AVE Days Inn Downtown 321 E FIFTH AVE Westmark Anchorage Hotel 720 WEST FIFTH AVE Inlet Inn 539 H ST The Voyager Hotel 501 K ST Anchorage Marriott Downtown 820 WEST 7TH AVE Marriott Residence Inn 1025 E 35TH AVE Sheraton Anchorage Hotel 401 E SIXTH AVE Hawthorn Suites 1110 W EIGHTH AVE Clarion Suites 325 W EIGHTH AVE Aspen Hotel 108 E 8TH AVE Inlet Tower Hotel & Suite 1200 L ST Anchorage Suit Lodge 441 E 15TH AVE Springhill Suites 3401 A ST Best Western Golden Lion 1000 E 36TH AVE Best Inns & Suites 4110 SPENARD RD Spenard Hotel Motel Inn 3960 SPENARD RD Chelsea Inn Hotel 3836 SPENARD RD Hampton Inn 4301 CREDIT UNION DR Puffin Inn 4400 SPENARD RD Best Western Barratt Inn 4616 SPENARD RD Holiday Inn Express 4411 SPENARD RD Best Value Inn Executive S 4360 SPENARD RD Millennium Alaskan Anchor 4800 SPENARD ROAD Hilton Garden Inn 100 W TUDOR RD Homewood Suites by Hilton 140 W TUDOR RD Fairfield Inn & Suites 5060 A STREET Coast International Inn 3333 W INT'L AIRPORT RD Courtyard by Marriott 4901 SPENARD RD Microtel Inn & Suites 5205 NORTHWOOD DR Arctic Inn Motel 842 W INT'L AIRPORT RD Dimond Center Hotel 700 E DIMOND BLVD ANC/TP41158.DOC/ E-1

86 HOTEL/MOTEL VISITOR MODEL ANC/TP41158.DOC/ E-2

87 Appendix F Screenlines ANC/TP41158.DOC/

88 APPENDIX F Screenlines (101) North of Tudor Minnesota to O Malley ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL MinnesotaDr_SB ArcticBlvd_SB CSt_NB DenaliSt_NB OldSewardHwy_SB NewSewardHwy_NB NewSewardHwy_SB MacInnesSt_SB LakeOtisPkwy_SB BragawSt_SB BonifacePkwy_SB BaxterRd_SB MuldoonRd_SB PattersonSt_SB Screenline Totals Counts Model MinnesotaDr_SB ArcticBlvd_SB CSt_NB DenaliSt_NB OldSewardHwy_SB NewSewardHwy_NB NewSewardHwy_SB MacInnesSt_SB LakeOtisPkwy_SB BragawSt_SB BonifacePkwy_SB BaxterRd_SB MuldoonRd_SB PattersonSt_SB ANC/TP41158.DOC/ F-1

89 SCREENLINES (201) North of Dimond Avenue Minnesota to Birch ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL MinnesotaDr_SB MinnesotaDr_NB CSt_NB thAve_EB LakeOtisPkwy_SB KingSt_SB NewSewardHwy_SB NewSewardHwy_NB OldSewardHwy_SB HomerDr_SB BraytonDr_SB DimondBlvd_EB AbbottLoopRd_SB Screenline Totals Counts Model MinnesotaDr_SB MinnesotaDr_NB CSt_NB 88thAve_EB LakeOtisPkwy_SB KingSt_SB NewSewardHwy_SB NewSewardHwy_NB OldSewardHwy_SB HomerDr_SB BraytonDr_SB DimondBlvd_EB AbbottLoopRd_SB ANC/TP41158.DOC/ F-2

90 (301) South of O Malley C Street to Hillside ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL CSt_SB OldSewardHwy_SB BraytonDr_SB NewSewardHwy_SB NewSewardHwy_NB ElmoreRd_SB BirchRd_SB HillsideDr_SB Screenline Totals Counts Model CSt_SB OldSewardHwy_SB BraytonDr_SB NewSewardHwy_SB NewSewardHwy_NB ElmoreRd_SB BirchRd_SB HillsideDr_SB ANC/TP41158.DOC/ F-3

91 SCREENLINES (401) South of Glenn Hwy Ingra to Muldoon ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL GambellSt_SB IngraSt_SB KarlukSt_SB AirportHeightsDr_SB TurpinSt_SB BonifacePkwy_SB BragawSt_SB McCarreySt_NB OklahomaDr_SB MuldoonRd_SB Screenline Totals Counts Model GambellSt_SB IngraSt_SB KarlukSt_SB AirportHeightsDr_SB TurpinSt_SB BonifacePkwy_SB BragawSt_SB McCarreySt_NB OklahomaDr_SB MuldoonRd_SB ANC/TP41158.DOC/ F-4

92 (501) West of Muldoon Tudor to Glenn Hwy ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL 36thAve_EB NorthernLightsBlvd_EB DebarrRd_EB MuldoonRd_SB NewGlennHwy_EB NewGlennHwy_WB thAve_EB BoundaryDr_EB Screenline Totals Counts Model thAve_EB NorthernLightsBlvd_EB DebarrRd_EB MuldoonRd_SB NewGlennHwy_EB NewGlennHwy_WB 6thAve_EB BoundaryDr_EB ANC/TP41158.DOC/ F-5

93 SCREENLINES (601) West of Boniface Tudor to Davis ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL DebarrRd_EB TudorRd_EB NorthernLightsBlvd_EB NewGlennHwy_WB DavisSt_EB NewGlennHwy_EB Screenline Totals Counts Model DebarrRd_EB TudorRd_EB NorthernLightsBlvd_EB NewGlennHwy_WB DavisSt_EB NewGlennHwy_EB ANC/TP41158.DOC/ F-6

94 (602) West of Birch Rabbit Creek to Abbott ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL RabbitCreekRd_EB DeArmounRd_EB HuffmanRd_EB OMalleyRd_EB AbbottRd_EB Screenline Totals RabbitCreekRd_EB DeArmounRd_EB HuffmanRd_EB OMalleyRd_EB AbbottRd_EB Counts Model ANC/TP41158.DOC/ F-7

95 SCREENLINES (701) East of Lake Otis Tudor to Commercial ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL TudorRd_EB ProvidenceDr_EB NorthernLightsBlvd_EB thAve_EB thAve_EB DebarrRd_EB MerrillFieldDr_EB CommercialDr_EB thAve_EB Screenline Totals Counts Model TudorRd_EB ProvidenceDr_EB NorthernLightsBlvd_EB 20thAve_EB 16thAve_EB DebarrRd_EB MerrillFieldDr_EB CommercialDr_EB 5thAve_EB ANC/TP41158.DOC/ F-8

96 (702) East of Lake Otis DeArmoun to Dowling ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL DeArmounRd_EB AbbottRd_EB HuffmanRd_EB OMalleyRd_EB thAve_EB ndAve_EB thAve_EB DowlingRd_EB thAve_WB Screenline Totals Counts Model DeArmounRd_EB AbbottRd_EB HuffmanRd_EB OMalleyRd_EB 80thAve_EB 72ndAve_EB 68thAve_EB DowlingRd_EB 84thAve_WB ANC/TP41158.DOC/ F-9

97 SCREENLINES (801) East of New Seward Hwy Rabbit Creek to 3 rd ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL 15thAve_EB BensonBlvd_EB NorthernLightsBlvd_EB thAve_EB thAve_EB rdAve_WB thAve_EB thAve_WB LoreRd_EB DimondBlvd_EB thAve_EB OMalleyRd_EB AcademyDr_EB OldSewardHwy_SB DeArmounRd_EB HuffmanRd_EB DowlingRd_EB TudorRd_EB thAve_EB Screenline Totals Counts Model 15thAve_EB BensonBlvd_EB NorthernLightsBlvd_EB 5thAve_EB 4thAve_EB 3rdAve_WB 9thAve_EB 6thAve_WB LoreRd_EB DimondBlvd_EB 68thAve_EB OMalleyRd_EB AcademyDr_EB OldSewardHwy_SB DeArmounRd_EB HuffmanRd_EB DowlingRd_EB TudorRd_EB 36thAve_EB ANC/TP41158.DOC/ F-10

98 (901) South of International Airport Road Minnesota to Lake Otis ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL MinnesotaDr_NB MinnesotaDr_SB ArcticBlvd_SB CSt_NB NewSewardHwy_NB NewSewardHwy_SB OldSewardHwy_SB HomerDr_SB BraytonDr_SB LakeOtisPkwy_SB Screenline Totals Counts Model MinnesotaDr_NB MinnesotaDr_SB ArcticBlvd_SB CSt_NB NewSewardHwy_NB NewSewardHwy_SB OldSewardHwy_SB HomerDr_SB BraytonDr_SB LakeOtisPkwy_SB ANC/TP41158.DOC/ F-11

99 SCREENLINES (1001) South of Dimond Minnesota to Birch ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL FrontageRd_SB MinnesotaDr_SB FrontageRd_NB MinnesotaDr_NB CSt_NB NewSewardHwy_SB NewSewardHwy_NB HomerDr_SB BraytonDr_SB OldSewardHwy_SB IndependenceDr_SB LakeOtisPkwy_SB BirchRd_SB Screenline Totals Counts Model FrontageRd_SB MinnesotaDr_SB FrontageRd_NB MinnesotaDr_NB CSt_NB NewSewardHwy_SB NewSewardHwy_NB HomerDr_SB BraytonDr_SB OldSewardHwy_SB IndependenceDr_SB LakeOtisPkwy_SB BirchRd_SB ANC/TP41158.DOC/ F-12

100 (2001) North of Eagle River Glenn Highway ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL GlennHwy_SB Screenline Totals Counts Model GlennHwy_SB ANC/TP41158.DOC/ F-13

101 SCREENLINES (2002) North of Eagle River Road Access Glenn to Birchwood ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL NewGlennHwy_NB NewGlennHwy_SB OldGlennHwy_NB Screenline Totals NewGlennHwy_NB NewGlennHwy_SB OldGlennHwy_NB Counts Model ANC/TP41158.DOC/ F-14

102 (2003) South of Hiland Glenn Highway ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL NewGlennHwy_NB NewGlennHwy_SB Screenline Totals Counts Model NewGlennHwy_NB NewGlennHwy_SB ANC/TP41158.DOC/ F-15

103 SCREENLINES (2005) North of 3 rd C Street to Port Access ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL OceanDockRd_NB PortAccessRd_NB PortAccessRd_NB Screenline Totals OceanDockRd_NB PortAccessRd_NB PortAccessRd_NB Counts Model ANC/TP41158.DOC/ F-16

104 (2006) West of Gambell 3 rd to 16 th ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL 16thAve_EB thAve_EB thAve_EB rdAve_EB thAve_EB thAve_EB thAve_EB Screenline Totals Counts Model thAve_EB 15thAve_EB 4thAve_EB 3rdAve_EB 6thAve_EB 9thAve_EB 5thAve_EB ANC/TP41158.DOC/ F-17

105 SCREENLINES (2007) South of 9 th L to Medfra ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL ASt_SB GambellSt_SB LSt_SB ISt_SB ESt_SB CSt_SB IngraSt_SB MedfraSt_SB Screenline Totals Counts Model ASt_SB GambellSt_SB LSt_SB ISt_SB ESt_SB CSt_SB IngraSt_SB MedfraSt_SB ANC/TP41158.DOC/ F-18

106 (2008) East of Ingra 3 rd to 15 th ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL 15thAve_EB thAve_EB rdAve_EB Screenline Totals thAve_EB 5thAve_EB 3rdAve_EB Counts Model ANC/TP41158.DOC/ F-19

107 SCREENLINES (2010) North of Fireweed/ Northern Lights Minnesota to Muldoon ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL ASt_SB MinnesotaDr_SB MinnesotaDr_SB SpenardRd_SB CSt_SB ArcticBlvd_SB NewSewardHwy_SB LakeOtisPkwy_SB BragawSt_SB BonifacePkwy_SB BaxterRd_SB MuldoonRd_SB Screenline Totals Counts Model ASt_SB MinnesotaDr_SB MinnesotaDr_SB SpenardRd_SB CSt_SB ArcticBlvd_SB NewSewardHwy_SB LakeOtisPkwy_SB BragawSt_SB BonifacePkwy_SB BaxterRd_SB MuldoonRd_SB ANC/TP41158.DOC/ F-20

108 (2013) East of Northwood Northern Lights to International Airport ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL IntlAirportRd_WB SpenardRd_SB McRaeRd_WB NorthernLightsBlvd_WB Screenline Totals Counts Model IntlAirportRd_WB SpenardRd_SB McRaeRd_WB NorthernLightsBlvd_WB ANC/TP41158.DOC/ F-21

109 SCREENLINES (2014) North of International Airport Spenard to Lake Otis ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL MinnesotaDr_SB CSt_NB MinnesotaDr_NB SpenardRd_SB NorthwoodSt_SB ArcticBlvd_SB NewSewardHwy_NB HomerDr_SB NewSewardHwy_SB OldSewardHwy_SB LakeOtisPkwy_SB Screenline Totals Counts Model MinnesotaDr_SB CSt_NB MinnesotaDr_NB SpenardRd_SB NorthwoodSt_SB ArcticBlvd_SB NewSewardHwy_NB HomerDr_SB 0 NewSewardHwy_SB OldSewardHwy_SB LakeOtisPkwy_SB ANC/TP41158.DOC/ F-22

110 (2016) West of Minnesota Raspberry to Klatt ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL DimondBlvd_WB thAve_WB KlattRd_WB StrawberryRd_EB RaspberryRd_WB Screenline Totals Counts Model DimondBlvd_WB 100thAve_WB KlattRd_WB StrawberryRd_EB RaspberryRd_WB ANC/TP41158.DOC/ F-23

111 SCREENLINES (2020) South End of Study Area New Seward Highway ABLKNME ABCOUNT BACOUNT TOTCOUNT ABCAP BACAP TOTCAP ABVOL BAVOL TOTVOL NewSewardHwy_SB Screenline Totals Counts Model NewSewardHwy_SB ANC/TP41158.DOC/ F-24