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/