A Deep Learning Based Multi-Block Hybrid Model for Bike-Sharing Supply-Demand Prediction

Miao Xu · Hongfei Liu · Hongbo Yang

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Urban Transport and Accessibility Traffic Prediction and Management Techniques Sharing Economy and Platforms

Abstract

As a new type of short distance commuting, the station-free sharing bike effectively alleviates urban traffic congestion. Thus, they are deployed in a large scale in many cities. However, various complex factors, including spatial, temporal, and other external information, result in serious imbalance of supply and demand between regions, which makes accurate prediction a challenging issue. In this study, our primary objective is to accurately forecast supply and demand by leveraging multi-source datasets. Based on the visual analysis about spatial-temporal characteristics of GPS data in Shanghai, we presented the innovative methods of Area of Interest grading and Traffic Analysis Zone division of bike-sharing, and revealed the distribution characteristics of sharing bike trips. A multi-block hybrid model where three blocks were separately modeled according to different data types was proposed. Moreover, eight state-of-the-art models and two variant models were developed as benchmarks to compare and evaluate the proposed model. The results suggested that MBH outperforms ten baselines with the highest accuracy. In addition, we conducted practical application of prediction results to validate that the proposed model could provide effective information for scheduling and rebalancing of bike-sharing system.

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References

2014Data-intensive applications, challenges, techniques and technologies: A survey on Big Data 2012Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use 2014The impact of weather conditions on bikeshare trips in Washington, DC 2018The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets 2013Exploring gendered cycling behaviours within a large-scale behavioural data-set 2019Modeling bike counts in a bike-sharing system considering the effect of weather conditions 2019Modeling bike counts in a bike-sharing system considering the effect of weather conditions 2017Modeling bike availability in a bike-sharing system using machine learning

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2022Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations 2024A bike-sharing demand prediction model based on Spatio-Temporal Graph Convolutional Networks 2025Explainable Graph Neural Networks for Optimized Allocation in Spatio‐Temporal Bike Sharing Demand Prediction 2023Determining the Sustainable Development Strategies and Adoption Paths for Public Bike-Sharing Service Systems (PBSSSs) under Various Users’ Considerations

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