Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93542
Title: | Short-term forecast of bicycle usage in bike sharing systems : a spatial-temporal memory network | Authors: | Li, X Xu, Y Chen, Q Wang, L Zhang, X Shi, W |
Issue Date: | Aug-2022 | Source: | IEEE Transactions on Intelligent Transportation Systems, Aug. 2022, v. 23, no. 8, p. 10923-10934 | Abstract: | Bike-sharing systems have made notable contributions to cities by providing green and sustainable mobility service to users. Over the years, many studies have been conducted to understand or anticipate the usage of these systems, with the hope to inform their future developments. One important task is to accurately predict usage patterns of the systems. Although many deep learning algorithms have been developed in recent years to support travel demand forecast, they have mainly been used to predict traffic volume or speed on roadways. Few studies have applied them to bike-sharing systems. Moreover, these studies usually focus on one single dataset or study area. The effectiveness and robustness of the prediction algorithms are not systematically evaluated. In this study, we propose a Spatial-Temporal Memory Network (STMN) to predict short-term usage of bicycles in bike-sharing systems. The framework employs Convolutional Long Short-Term Memory models and a feature engineering technique to capture the spatial-temporal dependencies in historical data for the prediction task. Four testing sites are used to evaluate the model. These four sites include two station-based systems (Chicago and New York) and two dockless bike-sharing systems (Singapore and New Taipei City). By assessing STMN with several baseline models, we find that STMN achieves the best overall performance in all the four cities. The model also achieves superior performance in urban areas with varying levels of bicycle usage and during peak periods when demand is high. The findings suggest the reliability of STMN in predicting bicycle usage for different types of bike-sharing systems. | Keywords: | Bike sharing Deep learning Prediction Shared mobility Travel demand |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on intelligent transportation systems | ISSN: | 1524-9050 | EISSN: | 1558-0016 | DOI: | 10.1109/TITS.2021.3097240 | Rights: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ The following publication X. Li, Y. Xu, Q. Chen, L. Wang, X. Zhang and W. Shi, "Short-Term Forecast of Bicycle Usage in Bike Sharing Systems: A Spatial-Temporal Memory Network," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 10923-10934, Aug. 2022 is available at https://doi.org/10.1109/TITS.2021.3097240. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Li_Short-term_Forecast_Bicycle.pdf | 3.16 MB | Adobe PDF | View/Open |
Page views
66
Last Week
0
0
Last month
Citations as of Apr 28, 2024
Downloads
29
Citations as of Apr 28, 2024
SCOPUSTM
Citations
13
Citations as of Apr 26, 2024
WEB OF SCIENCETM
Citations
13
Citations as of May 2, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.