Evolution of Fixed Transit to Microtransit Services: A Case Study in Annapolis, Maryland

In 2024, Maryland’s capital city Annapolis transitioned two underutilized fixed bus routes serving 133 stops to a microtransit service offering on-demand transportation among 237 stops. Microtransit uses advanced communication and scheduling technologies to dynamically adjust vehicle routes and schedules based on real-time passenger requests. In this paper, we present an mixed integer programming approach to replace the previous insertion heuristic in Annapolis’s microtransit operations. Based on one month of operational data, this new approach resulted in a significant increase in the number of trips served, with an average daily rise of 6% in one service area and 19% in the other. Simultaneously, vehicle miles traveled per served trip decreased by an average of 14% in one area and 31% in the other, demonstrating a more efficient use of resources, as the system serves more passengers while reducing the distance traveled per served trip. Moreover, microtransit offers a more personalized service, which tends to attract additional passengers due to its flexibility and convenience. However, as demand increases, the system can become overloaded, resulting in a significant number of trip requests being rejected. In such cases, the city of Annapolis may need to revert from microtransit to fixed bus routes. To support this fallback strategy, we further propose a mathematical programming model based on the orienteering problem to redesign the city’s fixed bus routes to best serve observed demand. Our simulation results show that the revised fixed bus routes not only accommodate a larger portion of the demand but also reduce passenger walking distances by 29% for one service area and 24% for the other.

History: This paper was refereed.

Funding: This work was supported by the National Science Foundation [Grant 2055347].