Station Importance Evaluation in Dynamic Bike-Sharing Rebalancing Optimization Using an Entropy-Based TOPSIS Approach

As an eco-friendly travel mode, bike-sharing has prevailed around the world. However, the systems are imbalanced due to the asymmetric spatial and temporal distribution of user demand. Station prioritization strategies are needed to rebalance more shared bikes for more important stations. This paper proposes an evaluation method of station importance in dynamic bike-sharing rebalancing. Firstly, a short-term demand prediction model is applied to capture the temporal and spatial characteristics of bike-sharing trip data and predict bike-sharing demand at the station level. Based on the prediction results, the method of determining rebalancing quantity is proposed with consideration of bike-sharing usage throughout the rebalancing period. Then, three criteria are employed to evaluate the importance of bike-sharing stations, including rebalancing quantity, closeness to inventory threshold, and distance from the key station. An entropy-based Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) approach is proposed to weigh different criteria and evaluate station importance. Furthermore, the experiments on bike-sharing data from Nanjing City demonstrate the effectiveness of the proposed methods. This research is helpful for operators and managers to dynamically rebalance shared bikes with high efficiency and improve the service quality of bike-sharing systems.

Automated summary

This paper proposes an evaluation method of station importance in dynamic bike-sharing rebalancing, using a short-term demand prediction model to predict bike-sharing demand at the station level and employing an entropy-based TOPSIS approach to evaluate station importance. Experimental results demonstrate the effectiveness of this method on bike-sharing data from Nanjing City.