Zimu Li

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.

Analyzing the traffic capacity of extra-long tunnels is crucial in assessing their sustainable capacity. However, previous studies on tunnel capacity mainly considered the influence of a single factor, ignoring the interaction between multiple factors, which cannot reflect the actual tunnel capacity. Therefore, considering the influence of multiple factors, this paper constructs an actual capacity calculation model for extra-long tunnels. Firstly, by combining hierarchical analysis and the entropy method, we determined the key factors that influence the capacity of extra-long tunnels. Secondly, based on the constructed traffic simulation model, we constructed an actual capacity model of extra-long tunnels by using multiple non-linear regression equations and tested the goodness of fit with the help of the misfit term. Finally, we determined the key correction coefficients of the model using the difference proportion method. Taking Qingdao Jiaozhou Bay undersea Tunnel as an example, the research results show that the method proposed in this paper can accurately determine the tunnel capacity with an error of less than 4%, providing a theoretical basis and practical guidance for the management and control of the tunnel’s sustainable carrying capacity after traffic congestion.