Dynamic demand-driven bike station clustering

Abstract

As an eco-friendly transportation option, bike-sharing systems have become increasingly popular because of their low costs and contributions to reducing traffic congestion and emissions generated by vehicles. Due to the availability of bikes and the geographically varied bike flows, shared-bike operators have to reposition bikes throughout the day in a large and dynamic shared-bike network. Most of the existing studies cluster bike stations by their geographical locations to form smaller sub-networks for more efficient optimization of bike-repositioning operations. This study develops a new methodological framework with a demand-driven approach to clustering bike stations in bike-sharing systems. Our approach captures spatiotemporal patterns of user demands and can enhance the efficiency of bike-repositioning operations. A directed graph is constructed to represent the bike-sharing system, whose vertices are bike stations and arcs represent bike flows, weighted by the number of trips between the bike stations. A novel demand-driven algorithm based on community detection is developed to solve the clustering problem. Numerical experiments are conducted with the data captured from the world's largest bike-sharing system, consisting of nearly 3000 stations. The results show that, with CPLEX solutions as the benchmark, the proposed methodology provides high-quality solutions with shorter computing times. The clusters identified by our methodology are effective for bike repositioning, demonstrated by the balance of bike flows among clusters and geographic proximity of bike stations in each cluster The comparison between clusters found in different hours indicates that bike sharing is a short-distance transportation mode. One of the key conclusions from the computational study is that clustering bike stations by bike flow in the network not only enhances the efficiency of bike-repositioning operations but also preserves the geographic characteristics of clusters.