Injury-Based Surrogate Resilience Measure: Assessing the Post-Crash Traffic Resilience of the Urban Roadway Tunnels

Abstract

Crash injuries not only result in huge property damages, physical distress, and loss of lives, but arouse a reduction in roadway capacity and delay the recovery progress of traffic to normality. To assess the resilience of post-crash tunnel traffic, two novel concepts, i.e., surrogate resilience measure (SRM) and injury-based resilience (IR), were proposed in this study. As a special kind of semi-closed infrastructure, urban tunnels are more vulnerable to traffic crashes and injuries than regular roadways. To assess the IR of the post-crash roadway tunnel traffic system, an over-one-year accident dataset comprising 8621 crashes in urban roadway tunnels in Shanghai, China was utilized. A total of 34 variables from 11 factors were selected to establish the IR assessment indicator system. Methodologically, to tackle the skewness issue in the dataset, a binary skewed logit (Scobit) model was found to be superior to a conventional logistic model and subsequently adopted for further analysis. The estimated results showed that 15 variables were identified to be significant in assessing the IR of the roadway tunnels in Shanghai. Finally, the formula for calculating the IR levels of post-crash traffic systems in tunnels was given and would be a helpful tool to mitigate potential trends in crash-related resilience deterioration. The findings of this study have implications for bridging the gap between conventional traffic safety research and system resilience modeling.

Evaluating the Development Levels of Green Urban Transportation Systems

“…The result should be a harmonious and sustainable urban transportation system for all elements of society that protects the environment, conserves resources, and helps the economy. It should involve safety, high efficiency, greenness, economy, wisdom, and tenacity [26,27].…”

Analysis of Factors Influencing the Severity of Vehicle-to-Vehicle Accidents Considering the Built Environment: An Interpretable Machine Learning Model

“…In exploring determinants affecting the severity of road traffic accidents, the academic literature primarily emphasizes six key dimensions: human factors, vehicle factors, road characteristics, crash characteristics, time, and environmental conditions [11]. Among them, human and vehicle factors include aspects such as driver's age [12], gender [13], driving experience [14], and vehicle type [15]; road characteristics involve elements such as the physical isolation of the road [16], road type [17], and other variables; crash characteristics include factors such as accident reason [18], cross-sectional location [19], whether it occurred at an intersection [20], and other elements; time and environmental conditions include variables such as seasonality [21], weather [22], climatic factors [23], and other variables. The relationship between traffic accidents and the built environment has been proposed since the last century.…”

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