An Investigation of the Transferability of Trip Generation Models Dr. Jerry Everett, University of Tennessee - CTR Dr. Fred Wegmann, University of Tennessee - CEE

Why try to Model and Forecast Future Travel? History tells that urban areas change over time The nature, location and magnitude of changes are almost always influenced by transportation The grand assumption is that if we can model existing travel then we can use that as a basis for forecasting future travel

“Inside the Black Box” The Role of Models in Transportation Planning Graphic borrowed from, A Transportation Modeling Primer - By Edward A. Beimborn

Can you Identify this location?

Same location about 45 years later

Motivation for this Research Smaller MPOs lack resources to gather local travel data Data/models are often borrowed or “transferred” from one area to another There is a need for a defensible, repeatable method for making transfer decisions Better models may lead to better decisions

History of Transferability of Trip Generation Models Validity of transferability has been debated since the infancy of travel demand modeling Studied several times using various methods over past 40 years –For example-Nashville and Knoxville were previously found to have similar trip rates. Consensus is maybe - in right circumstances

Phase I: Transferability Research Objective: Compare MPO travel rates within Tennessee Problem: No travel survey data available for small MPO Areas Solution: Conduct surveys for two small MPO areas

Study Areas Madison County, 560 sq. mi. Hamblen and Jefferson Counties, 490 sq. mi.

Survey Procedures RDD sample developed Advance letter mailed Recruited via CATI, hh & person data gathered Information packet and diaries mailed Reminder call prior to travel day Travel activity data retrieved via CATI Data quality checked then incentives mailed if applicable

Survey Diary

Survey Scope JacksonLakeway HHsPersonsDatesHHsPersonsDates Oct June Oct May 2007

Distribution of Trips by Purpose

Results: Average HH Trip Rates JacksonLakeway HBW HBO NHB Total Trips

Statistical Analysis where, X ij is the cell mean of the ith row and the jth column of the trip rate matrix for Area 1. Y ij is the cell mean of the ith row and the jth column of the trip rate matrix for Area 2. S 2 x ij is the cell variance of the ith row and the jth column for Area 1. S 2 y ij is the cell variance of the ith row and the jth column for Area 2. n ij is the number of observations (samples) in the ith row and the jth column for Area 1. m ij is the number of observations (samples) in the ith row and the jth column for Area 2. The null hypothesis is that the matrices’ cell means are not significantly different The test statistic will be compared to Chi Sq for i x j degrees of freedom at 0.05 If Q is larger then the hypothesis can be rejected signifying that the trip rates for the two areas are in fact different

Findings: Phase I Trip production rates for Jackson and Lakeway are not statistically different and thus transferable Most trip rates for Jackson and Lakeway are statistically different from Knoxville and Nashville Streamlined household travel surveys can be conducted for about $75* per complete (*Price did not include full geocoding, analysis and project management)

Phase II: Transferability Research Some remaining questions include: 1) Are differences real: due to HHs characteristics or the urban context? OR due to differences in data collection procedures? 2) Are transfers valid as long as the areas are similarly sized?

Phase II: Research Objective Test which is more appropriate: Transferring from a similarly-sized urban area in a different state/region of the country or Transferring from an urban area of a different size that is located within the same state?

Tennessee Study Areas

Ohio Study Areas

MPO Area Level Results 11 comparisons had zero differences 10 had a difference in one trip category 15 had differences in two trip categories 4 had differences in three trip categories 40 = Total area to area comparisons All 11 with no differences were within-state comparisons and all 11 pairs had data collected from a common survey

Trip Category Level Results

Findings: Phase II Considerable divergence was discovered so transferability can NOT be advanced as statistically valid on a general basis. The results were too mixed to provide clear guidance regarding the selection of an area from which to transfer. Though not statistically tested it is very likely that a large portion of differences found were due to survey procedures rather than actual differences in travel.

Issues with Evaluating Transferability of Trip Generation Definition of eligible HH survey participants varies Elements related to when data are collected vary Survey procedures and quality are often unknown Unusual circumstances may exist but not be documented Trip/activity: purpose definitions and categories vary

Phase III: Transferability Research Objective: Investigate if a meaningful measure of urbanization can be included in trip generation models from different areas to reduce differences between models and thus improve transferability?

Development of Area Types Purpose: Add missing dimension to transferability analysis Measure: Area Type – “Rural”, “Suburban” and “Urban” Approach: Population density based on 1 sq. mi. grids Implementation: HHs were attributed with pop density of grid cell in which they are located. Categorization Algorithm: TransCAD “Optimal Breaks”

Application of Area Type Measure Dayton, OH Mansfield, OH Rural – 826 HHs Suburban – 366 HHs Urban – 112 HHs Rural – 688 HHs Suburban – 898 HHs Urban – 364 HHs

Analysis Including Area Type A sample of 7 pairs of areas where trips rates were found to be different were re-compared These new comparisons were by trip purpose and disaggregated by the area type. The same statistical analysis was used as with previous comparisons

Results When Area Type is Included Note: Each of these comparison failed the prior test when area type was not included

Findings: Phase III A straightforward and meaningful measure of urbanization can be included in trip generation models using area type. Including area type significantly improves the results of the transfers. The procedures, tools and types of data used in this research are readily available to practitioners and are straightforward enough to be implemented by analysts in many small and medium-sized MPOs.

Summary & Conclusions Transferability is not as straightforward as hoped Methods are available to improve transferability results The standardization of survey practices could save costs and improve transferability analysis Additional research is needed on the topic