Enhancing the utilization of public bike sharing systems using return anxiety information

Abstract

Public Bike Sharing Systems (PBSSs) have become popular as mass transportation tools. Along with the growth of the population using them, disadvantages of PBSS are emerging, for example, when there are no bikes available for users to rent or no vacant parking lots to which bikes can be returned. To make up for this disadvantage, users are allowed to apply for fee discounts and obtain real-time information in advance from the Internet. The two main problems are full stations or lack of available bikes. But in hot spots or at specific hot times, even when users know the current station information, it is not possible to guarantee whether there will be parking lots or available bikes in the next second.To prevent the decline of the reference value of real-time bike information caused by large numbers of users, we collect the number of bikes near stations and real-time information in bike stations to form return anxiety information. When a station is nearly full or there are many bikes near the station, the return anxiety information value will increase. When users enquire about returning bikes to stations, they can refer to the return anxiety information. The higher the return anxiety information value, the lower the user’s tendency to return the bike. In the implementation, our study develops an android application to collect basic data and uses a Petri Net to simulate the PBSS scenario. Various rates of user trust are used to verify the reference value of return anxiety information.