Given the fast growth of on-demand transportation services and ride-sharing platforms, the concept of private vehicle ownership is rapidly declining. Although there are multiple fully-grown ride-sharing systems, they are proprietary and centrally controlled. Facilitating ride-sharing using a localized distributed coordination between the riders and the drivers is in need. However, fully distributed systems deal with a large number of variables and objectives and are often sub-optimal. In this paper, we propose a distributed ride-sharing system with multiple objectives which are often conflicting to each other. Therefore, we model it as a multi-objective optimization problem and solve it using the Ant Colony optimization technique which sports a multi-agent behavior. We critically analyze the spatio-temporal challenges posed by the ride sharing problem and define novel performance metrics to capture the underlying subtlety of the distributed system performance. An in-depth experimentation with recent large-scale single-ride taxi trip data from Chicago shows that our solution can ensure up to 79.65% success rate of ride sharing. We have shown that ride sharing is more successful during non-peak traffic hours due to less contention and a healthy balance in passenger and taxi numbers. Further, it has been observed that ride-sharing always reduces the total distance travelled by all the taxis and the total number of taxis on-road; both of which positively impact road congestion and environment. The results obtained from the experiments are very much comparable to real time behaviour of taxi networks. Finally, a revenue framework is proposed to analyse nuances of the operating environment.
CARE-Share: A Cooperative and Adaptive Strategy for Distributed Taxi Ride Sharing
IEEE Transactions on Intelligent Transportation Systems ; 23 , 7 ; 7028-7044
2022-07-01
4172243 byte
Article (Journal)
Electronic Resource
English
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