The role of system design attributes and community characteristics in microtransit use: Evidence from five microtransit systems

This study identifies the neighborhood, built environment, land use, and service design attributes that play a significant role in microtransit demand based on an econometric analysis of five microtransit systems operating in diverse areas. Pooled and site-specific hurdle negative binomial models estimated at a fine spatiotemporal scale are compared to reveal the factors that consistently contribute to higher microtransit use as well as the variation of their influence. Our results show that service reliability and availability, reflected in waiting times and operating hours, have a relatively greater influence on microtransit demand, compared to trip cost. Offering curb-to-curb service versus requiring riders to walk to pick-up and drop-off locations also has a large influence on the number of microtransit trips generated from and attracted to a census block. In terms of community characteristics, our findings indicate that lower-income and carless households generate more microtransit trips. In addition, we find a strong positive association between the proportion of female populations and microtransit trips, potentially because women are less likely to hold a driver’s license or have regular access to a vehicle in vehicle-deficit households. Younger populations, who often have greater travel needs and are more adaptable to technological advancements, also play a significant role in driving higher demand for microtransit. Moreover, proximity to residential, commercial, and healthcare land uses significantly contribute to more passenger trips. Policy implications underscore the importance of tailoring microtransit services to meet the specific needs of female riders, who often travel with younger children, and implementing targeted outreach initiatives to familiarize senior citizens with this service. Transit agencies are encouraged to consider the critical role of extended service hours for accommodating a broader range of travel purposes and schedules for low-income households, such as evening shifts. Strategic analysis of microtransit trip data is also important for identifying high-demand corridors that could be served by other transit modes for maintaining lower waiting times and improving efficiency in microtransit systems.