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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