00676677

Component

http://onlinepubs.trb.org...trr/1994/1452/1452-003.pdf

https://scholar.google.co...dier&publication_year=1994

For each class of transportation control measures (TCMs), the relevant travel behaviors expected to change are identified and techniques for simulating these changes are listed. Then, the latest round of analysis of TCMs in each of the four largest urban regions in California is studied carefully to see whether the relevant behaviors were modeled in a credible fashion, on the basis of local data. In modeling TCMs that change travel time and costs or expand transit options, models were found to lack automobile ownership steps and accessibility variables in some steps. Intersection capacity and delay should be entered into the road networks, and the networks need to be more detailed. In addition, more cost data are needed. Household income should be retained in final trip tables to allow for equity evaluations of changes in travel patterns. In simulating policies that change land uses, walk and bicycle modes should be explicit, and better land use data are needed. For analysis of clean vehicle incentive programs, vehicle types should be linked to trip purposes. Most agencies did a poor job evaluating TCMs; in some cases, they did not even use their travel demand models but instead used spreadsheets with generalized default values. Many improvements are being made to these models, and practice will be improved.

This paper appears in Transportation Research Record No. 1452, Travel Forecasting and Supply Models. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved

01401254

English

p. 18-26

1994

Transportation Research Record

References (25) ; Tables (1)

Accessibility; Accuracy; Air quality management; Automobile ownership; Automobiles by type; Bicycles; Costs; Equity; Highway capacity; Incentives; Income; Information, data, and knowledge; Intersections; Land use; Metropolitan planning organizations; Simulation; Traffic delays; Travel behavior; Travel demand management; Travel patterns; Trip purpose; Urban areas; Walking

Cost data; Data needs; Intersection capacity; Vehicle type

California

Data and Information Technology; Finance; Highways; Pedestrians and Bicyclists; Planning and Forecasting; Public Transportation; Society; I72: Traffic and Transport Planning

TRIS, TRB

Apr 27 1995 12:00AM

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• FULLY INCREMENTAL MODEL FOR TRANSIT RIDERSHIP FORECASTING: SEATTLE EXPERIENCE
• REAL-TIME INCIDENT-RESPONSIVE SYSTEM FOR CORRIDOR CONTROL: MODELING FRAMEWORK AND PRELIMINARY RESULTS
• SIMULATION MODEL FOR EVALUATING THE PERFORMANCE OF EMERGENCY RESPONSE FLEETS
• TRANSPORTATION POLICY ANALYSIS USING A COMBINED MODEL OF TRAVEL CHOICE
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• WILL MULTIMODAL PLANNING RESULT IN MULTIMODAL PLANS?