Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)

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Predicting Shared-Bike Routes with Geographic Information System and LSTM Algorithm

Authors
Hanfeng Wang1, Liangbo Zhang2, *, Ge Zhan3
1Division of Science and Technology, Beijing Normal University-Hong Kong Baptist University (BNU - HKBU United International College) Zhuhai, Guangdong, China
2School of Economics and Management, Harbin Institute of Technology (Shenzhen), Shenzhen, China
3AI Data Analytics Lab, Beijing Normal University-Hong Kong Baptist University (BNU - HKBU United International College) Zhuhai, Guangdong, China
*Corresponding author. Email: 20b957003@stu.hit.edu.cn
Corresponding Author
Liangbo Zhang
Available Online 10 November 2022.
DOI
10.2991/978-94-6463-005-3_57How to use a DOI?
Keywords
Shared bike; information system; algorithm; neural network
Abstract

Shared bikes in red, orange, yellow and blue can now be seen everywhere in China’s first-tier cities, Including Beijing, Shanghai, Guangzhou and Shenzhen. During the rush hour, because a large number of bikes are riding away, there is little reverse inflow, resulting in tidal dilemma. We collected over one million shared bike tracks and conducted data mining with our geographic information system to find out the rules of scientific parking of shared bikes. This project adopts a two-layer LSTM (Long and Short memory neural network) algorithm with spatial-temporal big data from Xiamen.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)
Series
Atlantis Highlights in Engineering
Publication Date
10 November 2022
ISBN
10.2991/978-94-6463-005-3_57
ISSN
2589-4943
DOI
10.2991/978-94-6463-005-3_57How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Hanfeng Wang
AU  - Liangbo Zhang
AU  - Ge Zhan
PY  - 2022
DA  - 2022/11/10
TI  - Predicting Shared-Bike Routes with Geographic Information System and LSTM Algorithm
BT  - Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)
PB  - Atlantis Press
SP  - 568
EP  - 575
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-005-3_57
DO  - 10.2991/978-94-6463-005-3_57
ID  - Wang2022
ER  -