Research on impact of short-term large-scale events on nearby traffic flow via interpretable machine learning

• Published in: Nanjing Xinxi Gongcheng Daxue Xuebao Vol. 16; no. 2; pp. 221 - 230
• Main Authors: Mingzhu, Wu, Kai, Feng, Jiancheng, Weng, Ruicong, Wei, Jingjing, Wang, Huimin, Qian
• Format: Journal Article
• Language: Chinese
• Published: Nanjing Nanjing University of Information Science & Technology 01.04.2024
• Subjects: Algorithms; Heterogeneity; Machine learning; Network control; Roads & highways; Synergistic effect; Traffic; Traffic flow; Weather
• ISSN: 1674-7070
• DOI: 10.13878/j.enki.jnuist.20230622001

The large number of people and vehicles gathered in a short period of time around large-scale events will lead to a differentiated traffic flow. Here, an interpretable machine learning model integrating XGBoost algorithm and partial dependence plots is proposed to capture the nonlinear effects and synergistic influences of large-scale events and their characteristics on the operation of nearby road network, and an empirical study has been conducted in Beijing. The heterogeneity of single factors shows that the distance of road section away from event venue and the event scale have great impact on nearby traffic flow, with relative importance of 27. 1% and 25. 4% , respectively ; time before start and after end of the event has obvious nonlinear characteristics, and the road sections within 3 km from the venue will be significantly affected within 30-60 minutes before the event and 30 minutes after the event. The synergistic effect of two-dimensional factors shows that, if an event attracted more than 30,000 p