Accident Rate Prediction Model for Urban Expressway Underwater Tunnel

Ruru Xing, Zimu Li, Xiaoyu Cai (), Zepeng Yang, Ningning Zhang and Tao Yang
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Ruru Xing: College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Zimu Li: College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Xiaoyu Cai: College of Smart City, Chongqing Jiaotong University, Chongqing 400074, China
Zepeng Yang: College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Ningning Zhang: College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Tao Yang: Chongqing Linggu Transportation Technology Co., Ltd., Chongqing 400064, China

Sustainability, 2023, vol. 15, issue 13, 1-28

Abstract: Urban tunnels often easily become traffic bottlenecks. Once traffic accidents occur, traffic congestion, environmental pollution, personnel and property damage and other problems restrict the sustainable development of tunnels. In order to reveal the factors affecting the incidence of tunnel traffic accidents, this paper quantitatively analyzes the influence of the single factors of tunnel geometric conditions and control measures on traffic accidents in Jiaozhou Bay underwater tunnel. The study examines the distribution patterns of tunnel traffic accidents under three dual-factor combinations: road gradient and curve radius, road gradient and slope length, and road gradient and the proportion of distance to the bottom of the slope. Based on this, a comprehensive index model is constructed using a negative binomial regression model to calculate the accident occurrence rate in Jiaozhou Bay underwater tunnel under road geometric conditions and control measures. The accident data after the second lining of Jiaozhou Bay underwater tunnel are selected as the validation object. The actual accident occurrence rate is compared with the model’s calculated value to verify the feasibility of the model constructed in this study. The results indicate that high gradient, long slope length, proximity to the bottom of the slope, and straight downhill sections have a significant impact on the occurrence of traffic accidents. Lane change signs can effectively reduce the accident rate by 30–40%. The percentage errors of the left- and right-lane traffic accident prediction models are within (−30%, 30%) and (−20%, 30%), respectively, which can assist in the design and control of underwater tunnels, offering valuable guidance and contributions to the construction of safer, more efficient, and more sustainable urban transportation systems.

Keywords: underwater tunnel; accident rate; prediction model; geometric conditions; control measures (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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