Predicting station locations in bike-sharing systems using a proposed quality-of-service measurement: Methodology and case study

Abstract

Bike-sharing systems (BSSs) operators tend to spend a great amount of time and effort to satisfy users. Accurately measuring the quality-of-service (QoS) of each station in a BSS will advance this mission. Moreover, measuring the QoS and using it to study the spatial dependencies in a BSS allows operators to better manage the system. The traditionally-known QoS measurement reported in the literature is based on the proportion of problematic stations, which are defined as those with no bikes or docks available to users. The authors investigated the traditionally-known QoS measurement, and it was found neither exposes the spatial dependencies between stations nor does it discriminate between stations in a BSS. This study proposes a novel QoS measurement, namely Optimal Occupancy that captures the impact of heterogeneity of bike-sharing systems (BSSs) and reflect the spatial dependencies between the stations. Optimal Occupancy is defined as the ratio of the total time a station is functional during a given interval to the length of the interval, in which it also redefines problematic stations. The authors applied geo-statistics to explore the spatial configuration of Optimal Occupancy variations and model variograms for spatial prediction. Results revealed that the Optimal Occupancy is beneficial for operators, would result in better prediction of the QoS at nearby locations, and can be used to predict candidate spots for new stations in an existing BSS. For example, the proposed QoS for Station 50 was improved after adding a new nearby station, increasing QoS from 0.52 to 0.84 for a Monday and Tuesday of July, respectively.<br/