Analysis and Application of Highway Tunnel Risk Factors Based on Traffic Accident Data

DOI

10.2991/978-94-6463-514-0_81How to use a DOI?

Keywords

Highway Tunnels; Risk Source Identification; Big Data Analysis; Risk Prevention and Control

Abstract

This paper aims to address the core challenge in highway tunnel operation safety: effectively identifying and forecasting complex, mutable risk sources to reinforce safety management and emergency response capabilities in high-traffic tunnels within Guangdong Province. By innovatively integrating big data analytics, this research deeply mines and quantifies the systemic, variable, and multi-layered features of tunnel operational safety risks, characteristics that traditional methods struggle to precisely capture. Initially, it consolidates and analyzes data from multiple tunnels across Guangdong, encompassing traffic volume, accident records, facility conditions, environmental variables, and management logs. Following this, leveraging advanced data analysis tools such as machine learning algorithms, these large datasets are cleansed, integrated, and patterns recognized to uncover underlying risk patterns. A risk indicator system is constructed systematically categorizing sources, including driver behavior, tunnel environment, facility condition, management effectiveness, and societal factors, ensuring comprehensive coverage of risk identification. The ultimate outcome establishes an interconnected “Driver-Vehicle-Tunnel-Management-Environment” risk source recognition system, which not only categorizes risks like driver violations, tunnel aging, management negligence, but also quantifies the likelihood and impact. Importantly, dynamic interdependencies among risks are revealed, such as peak hour traffic relevance with frequent accidents and delayed management responses exacerbating risks. The innovation lies in the application of big data analytics for refined, systemic risk source identification in tunnel operations, providing a scientific and technical framework for managing complex system safety and emergency response in similar scenarios.

Copyright

© 2024 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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TY  - CONF
AU  - Weiqun Hu
AU  - Jinyu Chen
AU  - Qingyan Tian
AU  - Chenchen Wang
AU  - Yanlong Zhang
PY  - 2024
DA  - 2024/09/28
TI  - Analysis and Application of Highway Tunnel Risk Factors Based on Traffic Accident Data
BT  - Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
PB  - Atlantis Press
SP  - 836
EP  - 849
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-514-0_81
DO  - 10.2991/978-94-6463-514-0_81
ID  - Hu2024
ER  -