Data-Driven Mobility Demand-versus-Offer Matching by Dynamic Integration of Public and Sharing Services

A systematic transition toward more sustainable transport solutions, included among the Sustainable Development Goals of the United Nations, is one of the main challenge modern cities must face. Increasing the usage of public transport and alternative mobility, like sharing services and micro-mobility, has been indicated as the most promising strategy since it can provide wide accessibility, reduce traffic congestion and consequently contain environmental impacts. Nevertheless, the citizen behavior shift toward such solutions in place of private motor mobility is hampered by several factors, among which quality of service emerge as one of the most relevant. To support decision makers in what-if analysis, tactical and strategic planning for improving of transport services, we present MODOM (MObility Demand-Offer Matching), a lightweight agent-based simulator that takes into account public transportation offer and sharing/micro-mobility solutions to evaluate the match with respect to the city users’ demand. MODOM supports dynamic updates of people’s travel plans to react to specific conditions and produces rich, decisiongrade KPIs. The simulator runs city-scale scenarios in few minutes, avoids time-consuming configurations, and is integrated with a Smart City Digital Twin for rapid what-if analysis, Snap4City. When needed for tactical planning, measured traffic can be injected by altering public transport timetables. Several experiments demonstrate that MODOM solves current simulator shortcomings and provides detailed simulation results to support decision making processes.