Real-time ridesharing operations for on-demand capacitated systems considering dynamic travel time information

Urban mobility is facing a paradigm shift towards providing more convenient, environmentally friendly, and on-demand services. Satisfying customer needs in a cost-efficient way has been the goal of many ridesharing systems. Capacitated ridesharing is considered as an effective service for reducing traffic congestion and pollution nowadays. Providing more operational strategies that can optimize on-demand ridesharing needs further investigation. In the current work, we focus on developing a matching algorithm for solving the on-demand ridesharing operation task in a real-time setting. We develop a simulation framework that can be used to propose a real-time shuttle ridesharing search algorithm. We propose a novel, computationally efficient, real-time ridesharing algorithm. We formulate the ridesharing assignment algorithm as a combinatorial optimization problem. The computational complexity of the proposed algorithm is reduced from exponential to linear, and the search space of the optimization problem is reduced by introducing heuristics. Our approach implements dynamic congestion by regularly updating the network's road segments' travel time during the simulation horizon to have more realistic results. We demonstrate how our algorithm, when applied to the New York City taxi dataset, provides a clear advantage over the current taxi fleet in terms of service rate. Furthermore, the developed simulation framework can provide valuable insights regarding cost functions and operational policies.