TY - JOUR
T1 - Simulating the effect of strategies to increase transit ridership by reallocating bus service
T2 - Two case studies
AU - Erhardt, Gregory D.
AU - Goyal, Vedant S.
AU - Kressner, Josephine
AU - Berrebi, Simon J.
AU - Brakewood, Candace
AU - Watkins, Kari E.
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/1
Y1 - 2024/1
N2 - We evaluate three strategies that transit operators might consider to increase ridership: a) increasing service on bus routes serving the highest share of low-income riders, b) increasing service on those bus routes with the highest ridership, and c) further providing the high-ridership routes identified in strategy (b) with exclusive bus lanes. In each scenario, we double the service frequency of buses on the focus routes and reduce the frequency on all other routes to maintain the total vehicle revenue miles, making the changes roughly cost-neutral. We tested these scenarios for Oshkosh, Wisconsin, and Atlanta, Georgia, using a modeling framework that combines CityCast, a commercially available data-driven planning tool to replicate observed travel patterns, and MATSim to simulate how travelers would change the route, mode, and time-of-day of the trips they make in response to the service changes. The results show substantial ridership gains for all but one scenario, suggesting that these strategies may provide a promising, low-cost means of increasing transit ridership in some contexts. However, impacts varied across the two case studies, indicating that local conditions play a role.
AB - We evaluate three strategies that transit operators might consider to increase ridership: a) increasing service on bus routes serving the highest share of low-income riders, b) increasing service on those bus routes with the highest ridership, and c) further providing the high-ridership routes identified in strategy (b) with exclusive bus lanes. In each scenario, we double the service frequency of buses on the focus routes and reduce the frequency on all other routes to maintain the total vehicle revenue miles, making the changes roughly cost-neutral. We tested these scenarios for Oshkosh, Wisconsin, and Atlanta, Georgia, using a modeling framework that combines CityCast, a commercially available data-driven planning tool to replicate observed travel patterns, and MATSim to simulate how travelers would change the route, mode, and time-of-day of the trips they make in response to the service changes. The results show substantial ridership gains for all but one scenario, suggesting that these strategies may provide a promising, low-cost means of increasing transit ridership in some contexts. However, impacts varied across the two case studies, indicating that local conditions play a role.
KW - CityCast
KW - MATSim
KW - Transit ridership
KW - Transport modeling
KW - Transport simulation
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U2 - 10.1016/j.jpubtr.2023.100080
DO - 10.1016/j.jpubtr.2023.100080
M3 - Article
AN - SCOPUS:85182909263
SN - 1077-291X
VL - 26
JO - Journal of Public Transportation
JF - Journal of Public Transportation
M1 - 100080
ER -