United States Department of Transportation
i
Technology Influence on Travel Demand and Behaviors [supporting datasets]
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2019-10-08
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[PDF-293.55 KB]
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Abstract:Over the last decade, the popularity of Transportation Network Companies (TNCs) as a mode of travel has been increasing at a steady pace, even in medium size cities. However, the determinants that influence transportation users to adopt TNCs as a preferred mode choice are still not well understood, nor are the impacts of such preferences on their travel patterns and transportation network operation. This study used a mixed methods approach to examine and document technology influence on travelers’ attitudes, preferences, and choices and their potential impact on transportation services in the Southeast. More specifically, the study investigated the influence of Transportation Network Companies (TNCs) such as Uber and Lyft, on travelers’ behavior in two medium size cities in the Southeast based on three distinct but interrelated case studies, in addition to a comprehensive literature review and synthesis. The first case study was a survey of 600 millennials (born 1981-1996) in North Carolina that was used to understand their travel behavior in a market where ride-hailing services have taken off in terms of use and coverage in the past 5 years. The second case study focused on factors that influence transportation users to select TNCs for completing typical day trips. A questionnaire survey was developed and used to survey over 450 transportation users in the Birmingham Metro area on their current travel preferences and practices and document their attitudes toward TNC use as a travel mode of choice. The third case study evaluated the feasibility of building an agent-based simulation model of the Birmingham Metro Area in order to study the impact of shifts in travel demand due to applications of shared-use economy on local and regional congestion. Due to the fact that commonly used traffic simulation models lack the ability to simulate shared modes in detail, the Birmingham prototype model was developed using the Multi-Agent Transport Simulation (MATSim) modeling platform and was a major undertaking in itself. The case study identified data needs and requirements for model development and adopted a data-driven approach for addressing data sparsity issues encountered. Future research by the research team will extend this work by expanding the prototype Birmingham MATSim model to incorporate public transit and quantifying the impacts from the integration of TNCs and transit on travel demand and congestion.
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Content Notes:As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset.
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