Abstract

Bike-sharing programs are increasingly popular as an effective way to enhance walk, transit, ride sharing, and car sharing accessibility. One common challenge is to find an efficient bike rebalancing strategy when pick-up and drop-off demands at bike stations are not evenly distributed in space and time. The goal of the rebalancing operation is to improve service level and reduce unsatisfied demand. Most, if not all, existing methods adopt approaches with adjustments based on spatial clustering and conventional network analysis techniques with a single criterion. This paper provides a ready-to-implement alternative to resolve the rebalancing problem with high model interpretability and tractability. The core concept of the proposed algorithm evolves from an observation that the solution set can be formed as a set of pickup-dropoff station pairs rather than individual stations. An unsupervised learning approach is used for parameter estimation and validation. The objective function assigns weights to the cost of the bike-redistribution van operation and the cost for unsatisfied demand. The algorithm contains three general steps with feedback. The first step converts the dynamic problem into a static problem using discounting method for dynamic demand; the second step assigns bike station pairs and finds the routing of each van stochastically; the third step converts the static problem back to dynamic to determine detailed operation variables such as the exact number of bikes to serve. Heuristic search and random perturbation for bike pair sequence is utilized to avoid the solutionsâ?? being trapped at local optima. The final validation using a training dataset and other data shows no overfitting problem for the models, and the results are consistent and efficient.