Identifying the Factors that Influence Urban Public Transit Demand

• URL: http://arxiv.org/abs/2111.09126v1
• Date: Tue, 16 Nov 2021 05:37:51 GMT
• Title: Identifying the Factors that Influence Urban Public Transit Demand
• Authors: Armstrong Aboah, Lydia Johnson, Setul Shah
• Abstract summary: The rise in urbanization throughout the United States (US) in recent years has required urban planners and transportation engineers to have greater consideration for the transportation services available to residents of a metropolitan region. These improvements can be achieved by identifying and understanding the factors that influence urban public transit demand. Common factors that can influence urban public transit demand can be internal and/or external factors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise in urbanization throughout the United States (US) in recent years has required urban planners and transportation engineers to have greater consideration for the transportation services available to residents of a metropolitan region. This compels transportation authorities to provide better and more reliable modes of public transit through improved technologies and increased service quality. These improvements can be achieved by identifying and understanding the factors that influence urban public transit demand. Common factors that can influence urban public transit demand can be internal and/or external factors. Internal factors include policy measures such as transit fares, service headways, and travel times. External factors can include geographic, socioeconomic, and highway facility characteristics. There is inherent simultaneity between transit supply and demand, thus a two-stage least squares (2SLS) regression modeling procedure should be conducted to forecast urban transit supply and demand. As such, two multiple linear regression models should be developed: one to predict transit supply and a second to predict transit demand. It was found that service area density, total average cost per trip, and the average number of vehicles operated in maximum service can be used to forecast transit supply, expressed as vehicle revenue hours. Furthermore, estimated vehicle revenue hours and total average fares per trip can be used to forecast transit demand, expressed as unlinked passenger trips. Additional data such as socioeconomic information of the surrounding areas for each transit agency and travel time information of the various transit systems would be useful to improve upon the models developed.

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