Agent-based Modeling and Simulation of Electric Taxi Fleets

Electrification and on-demand services are one of the main driving forces within the current automotive sector. This paper presents an approach to modeling and simulation of on-demand applications on the example of an electric taxi fleet. With regard to the high daily mileage and just the same idle times, the characteristic mobility behavior of taxis offers ideal conditions for electrification. To support decision-making during strategic and operational planning, this paper suggests a stochastic model based on an agent-based simulation approach. The simulation engine consists of an event-driven architecture. Customer demand requests, a customizable fleet configuration and infrastructure settings form the main input interface. The simulation output describes use of the infrastructure and the spatial and temporal behavior of each agent. We verify our basic model design first with a combustion engine powered taxi fleet. An additional scenario with electric vehicles provides insights into feasible electrification strategies for the taxi system in Munich. The strength of the proposed model is its distributed, behavior-driven architecture. This is especially useful for on-demand fleets, as these mobility systems are characterized by a mixture of centralized and decentralized knowledge bases. The whole system behavior results from dynamic decision making. Our approach can be used to evaluate various mobility-as-a-service concepts.