The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads.
@cite_1 introduced a ridesharing scheme considering a decentralized agent based system. In this paper, passenger agents try to find a partner every two minutes among driver agents. They delineated that given a high number of drivers, travel time for a single passenger decrease. We face a problem where passengers use taxis to commute not their own vehicles. Additionally, there is no emphasize on optimizing in @cite_1 research.
@cite_1
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This paper studies a dynamic dial-a-ride problem bearing complex constraints on a time-dependent network. A flexible scheduling scheme is proposed to dynamically cope with different stochastic events, such as the travelling time fluctuation, new requests, absences of customers, vehicle breakdowns, cancellations of requests, traffic jams and so on. A fast heuristic is proposed to re-optimize the schedule when a new event occurs. This heuristic consists of a properly organized local search strategy and uses a secondary objective function to drive the search out of local optima. Intensive computational simulations were carried out to evaluate the performance of this scheduling scheme and the influence of different stochastic factors. The simulation results of different scenarios with different percentage of dynamic requests reveal that this scheduling scheme can generate high quality schedules and is capable of coping with various stochastic events.