The Reliability Model for Bike‐Sharing Dispatch Based on Hotspot Detection and Hypothesis Test: A Case Study in Beijing

Abstract

In this article, a novel reliability model for bike-sharing dispatch is established using a hypothesis test. Based on the bike-sharing trajectory data from hotspot detection, we first perform the kernel density analysis to identify the dispatch points. As a result, a buffer area of 500 meters radius is designated as the studied dispatch area. From a systematic perspective, the reliability of the dispatch system is user-oriented during an ideal period when shared bikes constantly enter and leave the area. We propose the performance function of bike-sharing dispatch, in which the difference between origin and destination (OD) is defined as the main parameter of the failure probability of the system. By adopting different distribution forms, including Poisson distribution, Rayleigh distribution, exponential distribution, normal distribution, and gamma distribution, we examine the distribution characteristics of OD differences. The maximum likelihood estimation (MLE) technique is applied for model calibration, and chi-squared statistics are used to identify the acceptance of the null hypothesis. Finally, we take Beijing city as a case to verify this model. The results show that among many distribution models, the fitting goodness of normal distribution is the best. According to the properties and parameters of the distribution functions, we solve the dispatch scale for bike sharing at different confidence levels, allowing the dispatch strategy to be more flexible. Moreover, we find that the variation of dispatch quantity across different time periods and locations follows a systematic fluctuating trend.