Taxi vs. demand responsive shared transport systems: An agent-based simulation approach

Public transport in urban and suburban areas is not always able to meet population's need of accessibility to jobs, education, health and other opportunities in terms of routes and frequencies; therefore, those who do not own a private vehicle, or who cannot afford individual public transport (e.g. taxis), are often in a condition of social exclusion. Taking advantages of new ICT tools and facilities, Demand Responsive Shared Transport (DRST) services can provide “on demand” transport gathering ride bookings of different users and routing a fleet of vehicles to satisfy passengers' needs while minimizing the cost for the operator. In this paper, different DRST service configurations are compared to taxi services to investigate their economic attractiveness and sustainability. This is done by using an agent-based simulation model applied to the case of Ragusa (Italy), a city with poor public transport supply, where an innovative DRST service has already been experimented. A set of 50 different scenarios has been simulated, by varying the numbers of vehicles and seat capacity, and considering different demand rates and route choice strategies of the vehicles. Results are analyzed according to different key performance indicators, mainly showing that the DRST system is more advantageous than taxis when dealing with higher demand rates. On the other hand, the efficiency of the DRST system is rather limited compared to taxis in the case of low transport demand and fleets with a small number of vehicles. Between high and low demand there is a balance between the taxi and the DRST systems, where one should deepen the analysis to identify optimal operational parameters. These results pave the way for further analyses to help the planning and design of intermediate transport services like DRST, which are able to bridge the gap between collective and individual transport in urban and suburban areas.