Evaluating Stable Matching Methods and Ridesharing Techniques in Optimizing Passenger Transportation Cost and Companionship

In this work, we propose a Game Theory-based pricing solution to the ridesharing problem of taxi commuters that addresses the optimal selection of their travel companionship and effectively minimizes their cost. Two stable matching techniques are proposed in this study, namely: First-Come, First-Served (FCFS) and Best Time Sharing (BT). FCFS discovers pairs based on earliest time of pair occurrences, while BT prioritizes selecting pairs with high proportion of shared distance between passengers to the overall distance of their trips. We evaluate our methods through extensive simulations from empirical taxi trajectories from Jakarta, Singapore, and New York. Results in terms of post-stable matching, cost savings, successful matches, and total number of trips have been evaluated to gauge the performance with respect to the no ridesharing condition. BT outperformed FCFS in terms of generating more pairs with compatible routes. Additionally, in the New York dataset with high amount of trip density, BT has efficiently reduced the number of trips present at a given time. On the other hand, FCFS has been more effective in pairing trips for the Jakarta and Singapore datasets because of lower density due to limited number of trajectories. The Game Theory (GT) pricing model proved to generally be the most beneficial to the ride share’s cost savings, specifically leaning toward the passenger benefits. Analysis has shown that the stable matching algorithm reduced the overall number of trips while still adhering to the temporal frequency of trips within the dataset. Moreover, our developed Best Time Pairing and Game Theory Pricing methods served the most efficient based on passenger cost savings. Applying these stable matching algorithms will benefit more users and will encourage more ridesharing instances.