Journal Article

A three-in-one dynamic shared bicycle demand forecasting model under non-classical conditions

Yuan, Guan, Peng, Yuzhong, Huang, Jiangtao, Wu, Tao, Li, He, Han, Nan, Cai, Hongguo, Qiao, Shaojie

2024

Overview

Bicycle sharing systems are becoming highly popular and there have accumulated a large volume of users’ trajectory data. In a bicycle sharing system, most users’ borrowing and returning behaviors are arbitrary. Importantly, the bicycle sharing system is affected by weather, time as well as other dynamic factors under non-classical conditions. These factors make the scheduling of shared bicycles imbalanced, which is a typical machine learning problem under non-classical conditions. In addition, imbalanced bicycle scheduling impacts user experiences and causes huge economic losses. In this study, we propose a three-in-one dynamic shared bicycle demand forecasting model under non-classical conditions. The proposed model contains three essential and specific techniques: (1) Clustering stations based on the idea of data field. The activity degree of a station is calculated by constructing a bicycle transition network. The stations’ geographical locations and the bicycle transition patterns are taken into consideration. Then, nearby stations with similar transition patterns are aggregated into a cluster based on data field. (2) Modeling dynamic factors under non-classical conditions. The Pearson correlation coefficient is used to choose the most relevant dynamic weather features from real data. These features are transformed into a three-dimensional vector by taking into account the historical demand for bicycles in the station cluster. (3) Predicting bicycle demand using a LSTM (Long Short-Term Memory) network with multiple features that learns the features from the three-dimensional vector. The bicycle demand in each cluster is forecasted in a real-time and dynamic fashion every ten minutes. These three techniques collectively achieve one goal: accuracy prediction with low errors in real-time response. Extensive experiments are conducted on real Citi Bicycle data to compare the proposed model with traditional machine learning algorithms as well as the state-of-the-art approaches. The results demonstrate the superior performance of the proposed model in different evaluation measurements.

• Publisher: Springer Nature B.V
• Subject: Algorithms / Bicycles / Clustering / Correlation coefficients / Economic impact / Forecasting / Geographical locations / Machine learning / Predictions / Real time / Scheduling / State-of-the-art reviews / Time response / User experience / Weather