An Analysis of Congestion Factors at Signalized Intersections using Interpretable Machine Learning
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- SUZUKI Hiroaki
- Graduate School of Engineering, Kyoto University
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- UNO Nobuhiro
- Graduate School of Engineering, Kyoto University
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- KANASAKI Tomoya
- Institute of Systems Science Research
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- NAKANO Soushi
- Institute of Systems Science Research
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- YASUDA Kouji
- Institute of Systems Science Research
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- NAKAGAWA Shinji
- Institute of Systems Science Research
Bibliographic Information
- Other Title
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- 解釈可能な機械学習を用いた信号交差点における渋滞要因分析
Description
<p>Traffic congestion on ordinary roads including national highways remains an important problem in traffic engineering. Although various kinds of big data have been accumulated through the development of ICT in recent years, their utilization is not necessarily sufficient. In this study, traffic conditions are investigated mainly at signalized intersections, which are the main bottlenecks on ordinary roads, using ETC2.0 probe data, and road structure and land use data are organized using other big data such as digital national land information. Integrating and using these multiple data, the factors of traffic congestion on ordinary roads are quantitatively analyzed. Machine learning model was constructed using XGBoost and interpreted by SHAP. As a result, it was confirmed that one-hour traffic volume and other factors affect the occurrence of congestion and left-turn lane length and other factors may lead to the reduction of congestion. In addition, the effect of extending the right-turn lane on reducing congestion was confirmed by predicting the effect of congestion countermeasures focusing on individual signalized intersections.</p>
Journal
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- JSTE Journal of Traffic Engineering
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JSTE Journal of Traffic Engineering 9 (2), A_305-A_316, 2023-02-01
Japan Society of Traffic Engineers
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Keywords
Details 詳細情報について
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- CRID
- 1390576689850478080
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- ISSN
- 21872929
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed