Social and economic burdens caused by truck-involved rear-end collisions are of great concern to public health and the environment. However, few efforts focused on identifying the difference of impacting factors on injury severity between car-strike-truck and truck-strike-car in rear-end collisions. In light of the above, this study focuses on illustrating the impact of variables associated with injury severity in truck-related rear-end crashes. To this end, truck involved rear-end crashes between 2006 and 2015 in the U.S. were obtained. Three random parameters ordered probit models were developed: two separate models for the car-strike-truck crashes and the truck-strike-car crashes, respectively, and one for the combined dataset. The likelihood ratio test was conducted to evaluate the significance of the difference between the models. The results show that there is a significant difference between car-strike-truck and truck-strike-car crashes in terms of contributing factors towards injury severity. In addition, indicators reflecting male, truck, starting or stopped in the road before a crash, and other vehicles stopped in lane show a mixed impact on injury severity. Corresponding implications were discussed according to the findings to reduce the possibility of severe injury in truck-involved rear-end collisions.

To enable the reliability theory to be further applied in roadway geometry design and safety evaluation, it is necessary to explore and establish the relationship between the failure index in reliability theory and the traffic accident rate. Based on the collected data, the risk ranking-based verification method and regression prediction model-based verification method considering the probability of driving failure were used to verify the relationship between the probability of driving failure and traffic accident rate. The Spearman’s rank correlation coefficient was calculated and the results show that there is a moderate correlation between the driving failure probability of the selected road segment and the traffic accident rate. Four regression prediction models for both two-lane and four-lane roads were established considering the probability of driving failure and four significant variables including segment length, annual average daily traffic volume, speed limit, and curve radius. The results show that the established regression prediction models can fit accident data well. This research verified and established the relationship between the probability of driving failure and the road traffic accident rate and pointed out that the traffic accident rate has a positive correlation with the probability of driving failure.

In this paper, a kind of underground passage automated freight transport system based on the autonomous dual-mode container truck is proposed, and the overall structure of the automated freight transport system, the allocation of the transportation equipment, the comprehensive design of underground passage, the comprehensive analysis of transportation organization, the reasonable layout of the freight transport terminal, and the design of connection with the overall operation of the port are studied. Taking the connection passage between the central operation area and the eastern operation area of Yantian Port under planning and construction as an example, this paper expounds the application of the underground automatic freight transport system design method in the underground container logistics system within the port area. Based on the current situation and the long-term planning requirements of Yantian Port, the overall route of the connection passage between the central operation area and the eastern operation area is studied; the dual mode transportation organization model based on the combination of autonomous driving and manned driving is designed, and the manned driving mode shall be adopted within the operation area. In the connecting tunnel, there is the interference-free, fully-enclosed, line-fixed, and no man’s land application scenario, and the autonomous driving mode shall be adopted. The corresponding ground switching area is proposed to match the dual-mode transportation system of the autonomous dual-mode truck; such switching area is composed of the mode switching area, truck storage area, and driver rest and management room. Compared with the traditional tunnels, the autonomous freight transport passage has obvious advantages in saving project cost and improving transport efficiency.

As critical nodes in urban transportation networks, the operational resilience of urban tunnels directly affects roadway network efficiency and public safety. Existing studies on tunnel resilience exhibit deficiencies in the attention paid to routine incidents, the interpretability of evaluation methods, and the robustness of underlying data. To address these gaps, this paper constructs a three-layer resilience evaluation framework (pre-event, during-event, and post-event) based on 1123 accident records, hourly traffic flow data, and expert scoring data from 10 urban tunnels in Shanghai in 2024. The framework integrates the Analytic Hierarchy Process (AHP) and min–max normalization to derive comprehensive resilience scores. Furthermore, two explainable artificial intelligence (XAI) techniques—SHAP (SHapley Additive exPlanations) feature importance analysis and K-means clustering—are introduced to identify key drivers of resilience differentiation and typical resilience patterns, respectively. Empirical results reveal that (1) half of the 10 tunnels achieve a B-level rating or above, while C/D-level tunnels still account for 50%, indicating considerable room for improvement; (2) SHAP analysis suggests that during-event absorption capacity contributes most significantly to resilience differences (50.24%), with the average traffic reduction rate being the most influential single indicator within this model; and (3) K-means clustering classifies the tunnels into four patterns: high-resilience balanced type, pre-event weak type, post-event weak type, and during-event weak type, each corresponding to distinct resilience enhancement strategies. The methodological framework developed in this paper effectively enables the quantification, interpretation, and pattern recognition of tunnel resilience, providing data-driven decision support for urban tunnel operation and management. It should be noted that the A–D ratings are sensitive to the normalization method: under an alternative normalization, 7 out of 10 tunnels change grade. The ratings should therefore be interpreted as relative to the selected sample rather than as absolute performance levels.

In order to increase the rationality and efficiency of critical points identification of road network, and improve safety monitoring level of the road network, the hierarchy division method of the road network was studied. Based on the analysis of influence factors of road hierarchical level, this paper presented four evaluation indexes: road safety, functional, traffic flow characteristics and road infrastructure features. Roads were evaluated and sorted using the utility function. The paper achieved a hierarchical division of road network using the sample clustering analysis method, showed the basic process of the hierarchical division method through an example, and provided the basis for the identification of the critical points of the road network in future.