Integrated Travel Demand and Accessibility Model to Examine the Impact of New Infrastructures Using Travel Behavior Responses
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 1
Abstract
The study aims to propose an integrated travel demand and accessibility model to examine the impact of new infrastructures on accessibility for households and employment. The cumulative opportunity measure and the spatial and temporal accessibility measure are used to describe region accessibility layout. And a zonal accessibility measure is proposed to measure attractiveness of the central business district (CBD). Further, accessibility measures are obtained by various transportation modes and times of day considering travel behavior and network traffic congestion. The complete methodology is demonstrated using a case study from the state of Maryland, US. Maryland Statewide Transportation Model is used to build an integrated travel demand and accessibility model. The investment in different projects for constrained long range plan (CLRP) (known as the vision for future growth) is compared to a case without transportation improvement but still has the same growth. Analysis results show that (1) due to lack of transit facilities, car accessibility is significantly higher than transit during peak hours; (2) because of traffic congestion, car accessibility is significantly lower during peak hours than off-peak hours, while transit is the opposite due to high frequency service during peak hours; (3) transit facility improvement can not only increase accessibility but also narrow the gap in accessibility between peak and off-peak hours; and (4) the affected region of accessibility is primarily concentrated in the metropolitan area. The study results demonstrate that it is necessary to fully assess the effect of planned transportation improvements on regional accessibility using the multimeasure accessibility analysis.
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Data Availability Statement
Some or all data, models, or code generated or used during the study were provided by the National Center for Smart Growth Research and Education. They are proprietary or confidential in nature and may only be provided with restrictions (e.g., anonymized data).
Acknowledgments
The research is supported by the National Natural Science Foundation of China (Grant No. 71804127) and the Fundamental Research Funds for the Central Universities (Grant No. 22120210009). The model employed in the research was a task from a project titled “Maryland Statewide Transportation Model (MSTM)” supported by Maryland State Highway Administration (SHA) and Maryland Department of Transportation (MDOT). The opinions and viewpoints expressed in this paper are solely those of the authors and does not relate to the aforementioned agencies. Thanks for the data provided by the National Center for Smart Growth Research and Education.
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 1 • January 2022
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© 2021 American Society of Civil Engineers.
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Received: Apr 8, 2021
Accepted: Aug 30, 2021
Published online: Oct 18, 2021
Published in print: Jan 1, 2022
Discussion open until: Mar 18, 2022
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