Spatial allocation of heavy commercial vehicles parking areas through geo-fencing
Journal of Transport Geography 117 巻
103876- 頁
2024-04-17 発行
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| ファイル情報(添付) | |
| タイトル ( eng ) |
Spatial allocation of heavy commercial vehicles parking areas through geo-fencing
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| 作成者 |
Wu Jishi
Jia Peng
Li Gen
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| 収録物名 |
Journal of Transport Geography
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| 巻 | 117 |
| 開始ページ | 103876 |
| 抄録 |
Inadequate parking planning for heavy commercial vehicles (HCV) exacerbates urban road congestion. As an effective means of parking management, geofencing that identifies the virtual boundary for geographic areas is essential to ensure these vehicles do not impede traffic and urban spaces. However, geofenced areas must be rationally designed to prevent mismatches between parking areas and real parking needs. This paper presents a data-driven approach that integrates the Spatial-temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) methods and a Gaussian mixture model for identifying and predicting potential parking areas for HCVs. Leveraging the HCV trajectory data and land use data in Shanghai, China, we characterize the spatial distribution of parking demand and create a probabilistic model to predict active HCV traffic patterns and the spatial confidence regions under varying land use conditions. The results show that clusters of HCV parking demand tend to congregate near ports, comprehensive transportation hubs, logistics centers, and commercial hubs. These clusters correspond to five distinct parking demand patterns (i.e., day-long HCV stops, morning peak time HCV stops, daytime HCV stops, afternoon peak time HCV stops, and nighttime HCV stops), each reflecting specific spatiotemporal characteristics. The geofenced spatial domain was found to be very sensitive to the timing of parking, emphasizing the importance of using advanced geofencing technologies. The methodological framework introduced in this study holds significant value for policymakers and HCV operators as it aids in determining parking at strategic levels, offering valuable insights and tools to enhance the effectiveness of parking management.
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| 著者キーワード |
Commercial vehicles parking management
Geo-fenced parking area
ST-DBSCAN clustering
Gaussian mixture model
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| 内容記述 |
This study has been partially supported by the project funded by the Ministry of Land, Information, Transport and Tourism (MLIT) regarding development of efficient logistics systems (reference number A2300392); The Japan Science and Technology Agency (JST) has established the Promotion of Science, Technology and Innovation Project (Reference number JPMJFS21). The 111 Project of China under Grant (Reference number B20082); The National Natural Science Foundation of China under Grant (Reference number 72174035); National High-end Foreign Experts Recruitment Plan of China (Reference number G2023193005L).
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| 言語 |
英語
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| 資源タイプ | 学術雑誌論文 |
| 出版者 |
Elsevier
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| 発行日 | 2024-04-17 |
| 権利情報 |
© 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
This is not the published version. Please cite only the published version.
この論文は出版社版ではありません。引用の際には出版社版をご確認、ご利用ください。
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| 出版タイプ | Accepted Manuscript（出版雑誌の一論文として受付されたもの。内容とレイアウトは出版社の投稿様式に沿ったもの） |
| アクセス権 | エンバーゴ期間中 |
| 収録物識別子 |
[DOI] https://doi.org/10.1016/j.jtrangeo.2024.103876
～の異版である
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| 備考 | The full-text file will be made open to the public on 17 April 2026 in accordance with publisher's 'Terms and Conditions for Self-Archiving' |
