Abstract To investigate the effects of different turbulence models on the fire plume characteristics of train fires in tunnels, we employed five turbulence models: (1) one single-equation model: Spalart–Allmaras (S–A); and (2) four two-equation models: k−ε, k−ω, improved delayed detached eddy simulation (IDDES) based on SST k−ω and large eddy simulation (LES). These models were adopted for the numerical simulation of train fire plumes in tunnels, and their outcomes were compared with those of experiments conducted on a reduced-scale train fire model in a laboratory setting. These findings highlight the substantial impact of turbulence model selection on the simulation of fire plumes resulting from train fires in tunnels. When a train fire occurs within a tunnel, it is observed that the longitudinal distributions of temperature, pressure, velocity and soot density on the tunnel ceiling exhibit asymmetry. Among the selected turbulence models, the LES model consistently provided predictions that closely aligned with the experimental data for both fire plume morphologies and tunnel ceiling temperatures. The findings will help address the current gap in turbulence model applicability studies in fire simulations and offer important references for high-precision fire dynamics simulations.

Abstract Heavy-haul railways play a vital role in freight transportation, and the health of the rails directly impacts the safety and efficiency of railway operations. The heavy axle loads and long train compositions of heavy-haul trains make the rail surface susceptible to damage such as rail corrugation, spalling and abrasion, threatening operational safety. To address the issue, this paper proposes a multi-source data fusion method for identifying rail surface defects on heavy-haul railways. First, complete ensemble empirical mode decomposition with adaptive noise is used to decompose vibration signals and extract multi-dimensional vibration features. Next, dynamic time warping is applied to align rail profile data and extract key geometric features. Then, the vibration features and profile features are fused using Relief-F to select the most discriminative features. Finally, a support vector machine is utilized for defect identification. Experiment results show that the proposed method achieves high accuracy in identifying rail surface defects, with an accuracy of 96.4%.

Abstract As crucial nodes in railway transportation networks, railway passenger stations can suffer significant casualties and adverse societal impacts during fire accidents. A numerical simulation test was conducted on fire smoke spread and personnel evacuation in a railway passenger station. The results indicate that the structural span and floor height of the passenger terminal influence the smoke conditions at a height of 1.8 m such that the critical thresholds for smoke visibility, smoke temperature and carbon monoxide (CO) concentration are not surpassed. However, the smoke in the vicinity of a fire source can affect the surrounding environment. Notably, within each waiting hall, the spatial extent of the influence of smoke temperature is broader than that of smoke visibility and CO concentration. Moreover, the orthogonal test analysis reveals that the factors influencing personnel evacuation time, in descending order of impact, are the number of gates opened, personnel density, baggage weight and personnel ratio. The number of gates opened is the key factor affecting safe personnel evacuation, with at least three gates needing to be opened to meet safety requirements for evacuation. In addition, a nonlinear prediction equation is established to relate the personnel evacuation time to various influencing factors, and its prediction accuracy is found to be more pronounced when the evacuation duration exceeds 100 s. Furthermore, regardless of the specific waiting hall where the fire originated or the effectiveness of the automatic sprinkler system, the smoke temperature has the most prominent influence on the time required for safe evacuation.

Abstract Existing highway emergency personnel allocation patterns exhibit imbalances in the workload of emergency personnel on some road sections. On certain road sections with low traffic volumes and minimal toll revenue, the operational costs for emergency personnel are excessively high. Therefore, an efficient and economical method is required for the allocation of highway emergency personnel to respond to complex and dynamic highway emergencies. We established an optimization model for emergency personnel allocation based on an improved emperor penguin optimization (EPO) algorithm. First, the model introduces assumptions regarding the resource consumption of highway emergency tasks, guaranteed task operation time and sequential order of task execution by emergency personnel. The highway emergency personnel allocation problem is described mathematically. The optimization objectives of the model include minimizing the weighted processing time of emergency events, maximizing the workload balance and minimizing deployment costs. Constraints on emergency resource allocation are also defined. We propose an improved EPO algorithm that focuses on the initialization and optimization stages, improving parameter configuration, position updating, iterative convergence and disaster evaluation. Data from a traffic management office in Kunming, China were used for case analysis. We focused on the relationship between emergency events and the locations of emergency protection sites. We also examined the allocation of resources for traffic and natural disasters. Additionally, the resource deployment costs before and after optimization were compared. The results demonstrate that the proposed model reduces the cost of resource allocation by 30.6% and greatly improves the optimization of highway emergency staffing.

Abstract Ice conditions on the North Sea Route are characterized by time-to-time variability; favourable ice conditions exist only in 3 out of 10 years. Navigation becomes complicated and dangerous in connection with the deterioration of weather and ice conditions: compression of ice, visibility decline, fall of temperature and other reasons. In this case, the person who makes the decision on the departure of a cargo ship from the port should choose one of three variants of the operations. First, traffic congestion before an improvement in the ice conditions; second, the departure from a port and independent navigation on the scheduled route; or third, navigation with icebreaker assistance. Each of the variants differs in the defined risk of accident and possible economic losses, including the idle time in a port and the costs of icebreaker assistance. This paper contains a possible approach to the decision: applying the yardstick of acceptable risk that allows considering probabilities of realization of the various scenarios of the ice conditions, the probability of emergency incidents, economic losses because of idle time or repair work after damage, and readiness of the decision maker to run acceptable risks for the acceleration of delivery of the onboard cargo.

Abstract Thorax injury is one of the main injuries during pedestrian impacts, and vehicle collision plays an important role in increasing the risk of pedestrian thorax injury. It is necessary to conduct in-depth research on the influence of vehicle front structural parameters on thorax injury in pedestrian accidents. Accident statistical analysis is conducted based on 452 cases from the Future Mobile Traffic Accident Scenario Study/FASS database. There are differences in the severity of thorax injuries caused by different types of vehicles. Then, a typical pedestrian thorax injury accident in the FASS database is simulated, and the effectiveness of the model is verified. Finally, 149 simulations are conducted based on the model, and the relationship between the front structural parameters and pedestrian kinematic response and thorax injury values is analyzed using multiple linear regression methods. The significant probability p of the influence of the bonnet angle/BA, bonnet length/BL, bonnet leading edge height/BLEH, and bumper bottom height/BBH on pedestrian thorax injuries are as follows: 0.000, 0.953, 0.000, 0.797, indicating that BA and BLEH have significant influences on thorax injuries, while BBH and BL have little influence. And when the BLEH and BA values increase, the thorax acceleration value tends to increase.

Abstract Due to the urgent demand for lightweight and high-strength materials in rail transportation, this study proposed foamed polylactic acid (PLA) composites reinforced with continuous basalt fibres using a 3D printing technique to address the limitations posed by foaming-induced strength reduction in foam. Through a combination of parametric calculations, microscopic observations and compression experiments, the effects of printing parameters on the expansion ratio and print accuracy of foamed composite were investigated. It was found that adding fibres to foamed PLA reduced the expansion ratio of PLA by up to 9.52% at lower printing temperatures and layer heights but increased it at higher settings. The expansion ratio of the composite significantly increased with high printing temperatures and layer heights. When the composites were fabricated at low print temperatures and high layer heights, noticeable interlayer gaps and exposed fibres leading to poor impregnation were observed at cross-section. This phenomenon was improved as the expansion ratio increased. In addition, specimens with optimal print accuracy were prepared at specific combinations of printing temperature and layer height. In light of this discovery, a predictive function based on combined printing parameters was established to design composites with excellent print accuracy and specific densities. Finally, compression test results showed that with the same density of 0.5 g/cm3, the foamed composite exhibited substantial improvements in compressive strength, modulus and strain energy density compared to the foamed PLA, with increases of 44.44%, 57.02% and 24.19%, respectively.

Abstract The embankment and the hill cut are conventional infrastructure scenarios along the railway line in windy regions. The windbreak walls with the same height are separately installed on them to guarantee the safety of train operation. However, due to the realistic terrain, the distances of windbreak walls on the embankment and hill cut from the railway centreline are not the same, generating a windbreak wall transition and causing a sudden change of the windproof performance, which contributes to the so-called ‘yawing’ phenomenon when the train passes through the windbreak transition. The current study aims to predict the flow characteristics around the existing right-angled windbreak wall, with three preliminary designs and four local improvement designs at the transition connecting the embankment and the hill cut along the high-speed railway. The detached-eddy simulation method with the realizable k-ε turbulence model was used, while the contour lines were applied to generate the realistic terrain. The velocity distributions in the existing windbreak wall transition present a fast change from the large negative value (i.e. −0.16U on windward line (WWL) and −0.20U on leeward line (LWL)) to the positive value (i.e. 0.47U on WWL and 0.42U on LWL) within the distance of 10h. The different locations of windbreak walls on the embankment and hill cut create a rectangular discontinuous transition. The consistency of the windproof performance is destroyed at the windbreak wall transition, resulting in a sudden gust on the railway lines. A preliminary design idea coming from the hypotenuse on a right triangle was proposed to smooth the transition from the windbreak on the cut to that on the embankment. Three included angles were discussed, and the 30° scheme shows the best shielding performance, achieving about 46% reduction on the peak-to-peak value on the WWL and 32% on the LWL at the middle height of the train. However, the positive peaks are still observed. On this 30° scheme, four local improvement designs are attempted to further smooth the velocity distributions. The best design that meets three wind directions can achieve about 80% reduction of peak-to-peak values. All achievements can provide a reference for the design of windbreak walls at different transitions and improve the safety of train operation under strong winds.

Abstract With the continuous development of vehicle intelligence technology and active safety technology, the autonomous emergency braking pedestrian (AEB-P) system has emerged as a crucial element of contemporary automotive active safety systems. However, there are still some deficiencies in the control strategies utilized by the current AEB-P system. It is imperative to design an AEB-P system that can adapt the safety threshold and control strategy in real time based on the current driving conditions. The objective of this paper is to propose an AEB-P system with an adaptive control strategy and analyse its effectiveness from the perspective of pedestrian head injuries. Initially, an algorithm for adaptive collision time threshold model is proposed, and the AEB-P control strategy integrating this threshold is designed. Subsequently, a finite element model of the human head is developed to predict pedestrian head injuries in pedestrian-vehicle collision (PVC) scenarios with AEB-P intervention. Additionally, a simplified finite element model of PVC is established, and real PVC scenarios are reconstructed to assess the effectiveness of various AEB-P control strategies in mitigating pedestrian head injuries. The results demonstrate that the conventional control strategy is ineffective in reducing the risk of pedestrian head injuries at medium to high speeds. Conversely, the AEB-P system, which incorporates an adaptive control strategy, proves to be effective in decreasing the risk of pedestrian head injuries. Overall, the AEB-P system with adaptive control strategy enhances pedestrian safety, particularly in scenarios where PVCs are inevitable.

Abstract Crash data is the foundation of traffic safety analysis, which can help experts find the cause of crashes and propose corresponding countermeasures. In China, the accident reporting form only allows reporting of one crash cause for each crash based on the prespecified crash cause code. This designation may lead to inaccuracy in recording crash data, especially for state-related crashes. The crash narratives, which are the responding officer's written account of what occurred before, during and after a crash, contain considerable free-form information associated with the crash occurrence. This study investigated the directly contributory factors behind state-related crashes through the development of natural language processing and deep-learning models based on 1625 state-related crash narratives. According to the directly causative factors described in the crash narratives, state-related crashes were labelled speed related, turning related and other causes. Then the crash narratives were vectorized for model training and frequency analysis. The text-CNN, LSTM, GRU and SVM models were applied to reclass the vectorized crash. The results showed that the text-CNN model showed the best model performance in text classification, and the AUC value of this model reached 0.90 for micro-average curves. The results from this study can engage the usage of crash narratives and help identify the actual causative reasons hidden behind some inaccurate crash value designations.