A Game Theory-based Pricing Technique for Ridesharing Pairings

As more people dwell in urban cities, intelligent transportation systems must cater to the ever increasing demand of public transport. The various modes of transportation should allow affordable and comfortable travel from one point to another in an urban city to discourage people in owning their own vehicle that may result to more traffic congestions and detrimental environmental effects. In this study, we employ empirical urban mobility datasets to evaluate a proposed Game Theory-based pricing method that can be used in ridesharing of two passengers to reduce traveling cost, while keeping the driver revenue approximately equal. We consider passenger pairings where a second commuter’s origin and destination points are after the first commuter passenger, thereby, creating an overlapped and shared travel distance between the two commuters. From extensive simulations, our Game Theory-based pricing scheme resulted in (1) approximately 30% less number of active taxi trips needed to transport people from origin to destination places, (2) slight increase in the travel distance of each passenger due to the optimization of shortest path that can cater both commuters (nearly 8% on the average for the three cities), (3) reduced average passenger fare (at least 15% fare reduction) when compared to solo riding, (4) little to no driver revenue increase, and (5) lower passenger fare rate, i.e., cost per kilometer.