A New Agent-Based Model to Simulate Demand-Responsive Transit in Small-Sized Cities

Innovative demand-responsive transport services are spreading in most urban areas, allowing dynamic matching between demand and supply and enabling travellers to request shared rides in real-time via mobile applications. They are used both as an alternative to public transport and as an access/egress leg to mass transit stations, i.e., acting as a feeder service. In low-demand areas and small-sized cities, it is often difficult to provide effective and cost-efficient public transport, thus resulting in an extensive use of private vehicles. Using an agent-based modelling approach, this study compares the performance of fixed-route transit (FRT) and demand-responsive transit (DRT), where optional stops can be activated on demand. The aim is to identify the conditions allowing DRT to become more advantageous than FRT in small-sized cities, both for travellers and the transport operator. A real-time matching algorithm identifies optimal trip chains (i.e., public transport lines; pick-up, drop-off and transfer stops; and time windows) for travel requests, dynamically updating vehicles’ routes and schedules. The model is applied to the city of Caltanissetta, Italy, where a transit service with six FRT urban lines is currently operating. Travel patterns were reconstructed from thousands of travel requests collected by a Mobility-as-a-Service platform within one-year. The main findings demonstrate the benefits of DRT in providing a higher quality of service, reducing riding times for passengers, and enhancing service efficiency without burdening operating costs. The DRT reduced the vehicle-kilometres travelled by up to 5% compared to FRT while decreasing passenger ride times by approximately 10%. An economic analysis showed reductions in operator unit costs of up to 3.4% for low-demand rates, confirming the advantages of flexible operations in small-sized cities.