Distributed Trip Selection Game for Public Bike System with Crowdsourcing

Public Bike Systems (PBSs) offer convenient and green travel service and become popular around the world. In many cities, the local governments build thousands of fixed stations for PBS to alleviate the city traffic jam and solve the last-mile problem. However, the increasing use of PBSs leads to new congestion problems in the form that users have, such as no bike to rent or no dock to return the bike. Further, users wish to receive assistance on deciding how to select bike trips with minimal time cost while taking congestion into account. Meanwhile, crowdsourcing attracted increasing attention in recent years. This paper applies it to help users share information and select bike trips before the bikes or docks are occupied. An interesting and important problem is how to help users select bike trips so that the time consumed on the trips can be minimized. We model the problem as a Bike Trip Selection (BTS) game which is shown to be equivalent to the symmetric network congestion game. This equivalence allows us to design a BTS algorithm by which the users can find at least one Nash Equilibria (NE) distributively. Furthermore, this paper evaluates the algorithm based on real datasets collected from the PBS of Hangzhou City in China. We also design a BTS system including an Android APP and a server to conduct the experiment for the distributed BTS algorithm in practice.