Train Collision Research Articles

Research on Response Postures of Subway Train in Straight Line Collision

A six-car subway train finite element model was developed to investigate possible response postures under impact accidents. Considering the plastic deformation of carriages and weak points of the train front, the simulation included two conditions: train–train collision and collision with an obstacle. Comprehensive response postures were obtained. A one-dimensional dynamic theory model was established to verify the rationality of the FEM model. The influence of impact speed, impact angle, and impact position on the energy consumption and response postures were discussed. The results show that one accident condition is accompanied by a variety of response postures. The main factors of climbing are asynchronism of yaw and pitch motions of adjacent carriages and plastic deformation of carriage ends, which leads to vertical arch. In oblique and front-side collision, the lateral force and the deviation of train front cause rapid derailment, and large lateral movement of the front and lateral buckling happen subsequently.

Study on the Influence of Collision Scene on the Energy Dissipation Process for Train Collision

Study on the Influence of Collision Scene on the Energy Dissipation Process for Train Collision

Study on Collision Dynamics Similarity of Thickness Distortion Model of Train Five-Hole Thin-Walled Structure

Full-scale train collision tests are rarely carried out because of high cost, but small-scale model tests have good economy and repeatability, providing a new research method for train crashworthiness design. The metal thin-walled tube is a commonly used energy-absorbing structure for trains. Its collision characteristics directly affect the impact resistance and collision behavior evolution process of trains. This paper studies the collision dynamics similarity of the small-scale model of a five-hole thin-walled energy-absorbing structure at the end of a high-speed train. Firstly, the dynamic mechanical behavior and governing equations of high-speed train collision were analyzed, and the applicable similarity criterion was derived by similarity transformation to develop a similarity scale model. According to the simulation, the energy absorption, peak force and other key responses of the full-scale prototype collision were analyzed. Then, the applicability of the thickness distortion scale model of the five-hole energy-absorbing structure in the high-speed train was proved by equation analysis and error analysis. The impact dynamic responses of the complete similarity model and the distortion similarity model were discussed. A correction program with collision deceleration as the dependent variable was proposed for the distortion similarity model, and the fitting relationship between the distortion factor and the correction factor was obtained. The results calculated by the proposed method prove that the peak force-deformation-energy absorption error of the thickness distortion similarity model in predicting the dynamic responses of the prototype collision is less than 8%, which means the thickness distortion scale model has good accuracy in the prediction of full-scale prototype collision dynamics responses.

A Hybrid Optimal Approach for Efficient Remote Monitoring and Avoidance of Train Collisions

A Hybrid Optimal Approach for Efficient Remote Monitoring and Avoidance of Train Collisions

The influence of forward leaning postures on passenger injury in urban rail train collision

The influence of forward leaning postures on passenger injury in urban rail train collision

Triple train collision at Bahanaga Bazar: Disaster response and management: An observational study.

Train collision accidents are tragic events associated with high mortality. The study aimed to comprehensively describe the clinical-epidemiological profile, disaster emergency response, and management following a train collision accident in Odisha, India. This observational study was conducted by a tertiary care hospital in eastern India. Data were collected from the hospital records, telephonically from the victims or their relatives, and from electronic and print media. Subjects with complete data were included. Those who had died on the spot or upon arrival to the emergency department (ED) and with incomplete or unavailable data were excluded. Statistical analysis of data was performed using R 4.1.0 software. After triaging, 198 subjects were included and analyzed. Most of the subjects were young (29 [34-40] years), belonging to male 182 (92%) gender. At ED triage, 41 (21%) subjects were categorized as red, 103 (52%) as yellow, 54 (27%) as green, and two subjects as black. Regarding the mode of transport to the ED, 57 (29%), 17 (9%), and 124 (63%) subjects were transported by ambulances, police vans, and private vehicles, respectively. Among the included subjects, 96 (48%), 65 (33%), and 49 (25%) had head injuries, maxillofacial injuries, and blunt chest trauma, respectively, and the majority (n = 185 [93.4%]) had associated external injuries. A coordinated effort by various government, nongovernment agencies, and local communities played an important role in the aftermath of the disaster to save lives. A well-trained medical emergency disaster response team involving multiple stakeholders can help mitigate such tragic incidents.

LiDAR-Based Urban Three-Dimensional Rail Area Extraction for Improved Train Collision Warnings.

The intrusion of objects into track areas is a significant issue affecting the safety of urban rail transit systems. In recent years, obstacle detection technology based on LiDAR has been developed to identify potential issues, in which accurately extracting the track area is critical for segmentation and collision avoidance. However, because of the sparsity limitations inherent in LiDAR data, existing methods can only segment track regions over short distances, which are often insufficient given the speed and braking distance of urban rail trains. As such, a new approach is developed in this study to indirectly extract track areas by detecting references parallel to the rails (e.g., tunnel walls, protective walls, and sound barriers). Reference point selection and curve fitting are then applied to generate a reference curve on either side of the track. A centerline is then extrapolated from the two curves and expanded to produce a 2D track area with the given size specifications. Finally, the 3D track area is acquired by detecting the ground and removing points that are either too high or too low. The proposed technique was evaluated using a variety of scenes, including tunnels, elevated sections, and level urban rail transit lines. The results showed this method could successfully extract track regions from LiDAR data over significantly longer distances than conventional algorithms.

CFD-DEM modelling of particle entrainment in wheel–rail interface: a parametric study on train characteristics

Rail-sanding is employed to improve the train’s wheel–rail traction loss in low adhesion conditions. This can significantly impede trains’ kinematics, operation, and performance by hindering the train’s acceleration and deceleration, resulting in delays and unreliability of transport system as well as causing safety risks and in the worst cases train collisions. Rail-sanding has its own merits in recovering the wheel–rail traction but can result in a sand wastage of more than 80% due to its low sand entrainment efficiency. In this research, computational fluid dynamics is coupled to discrete element modelling to study the behaviour of sand particles during rail-sanding. A parametric study based on the train characteristics, including train velocity, sand flow rate, and the geometry of the sander nozzle, is performed by comparing the entrainment efficiency of the sand particles. It is found that train velocities over 30 m/s result in the entrainment efficiency of almost zero. A moving air layer generated at the wheel–rail interface influences the lower bound of acceptable particle size range. The flow rate and nozzle geometry can be designed to enhance entrainment efficiency.

Head injury mechanisms of the occupant under high-speed train rear-end collision

To improve the passive safety of high-speed trains, it is very important to understand the mechanism of head injury in high-speed train collisions. In this study, the head injury mechanisms of occupants in high-speed train rear-end collisions were investigated based on the occupant-seat coupling model, which included a dummy representing the Chinese 50th percentile adult male. The typical injury responses in terms of skull fractures, brain contusions, and diffuse axonal injury (DAI) were analyzed. Meanwhile, the influences of collision speed and seat parameters on head injury response were examined. The simulation results indicate that the skull fractures primarily occur at the skull base region due to excessive neck extension, while the brain contusions and DAI result from the relative displacement of different brain regions. The increase in collision speed will promote the probability of skull fracture, brain contusion, and DAI. Seat design modifications, such as reduced seat spacing, increased seat backrest angles, and selecting the appropriate cushion angle (76°) and friction coefficient (0.15), can effectively mitigate probably occupant's head injury.

Train Collision Avoidance System with Wireless Communication

Train Collision Avoidance System with Wireless Communication

Investigation on similitude method for dynamic response of high-speed train body considering thickness distortion

Small-scale model test is an economical and efficient method to study the collision process analysis and crashworthiness design of full-scale high-speed trains. For high-speed trains, the vehicle body thickness of scaled train models is too small to be processed, resulting in thickness distortion. Although the distorted model can predict dynamic responses of the full-scale model, there are some errors in predicting the dynamic response parameters. This article proposes a new similitude distortion method to improve the prediction accuracy of the high-speed train body distorted model. First, the complete similitude relationship for train collisions was derived using dimensional analysis. According to the Buckingham π theory, the theoretical expression between prediction coefficient and distortion coefficient was obtained when the high-speed train thickness distortion occurred. Then, based on a full-scale high-speed train body prototype, the benchmark model and distorted model were established. Numerical simulation was used to explore the relationship between prediction coefficient and distortion coefficient. Finally, the distorted similar model was established using the similitude distortion method. The accuracy and feasibility of this approach were verified by comparing and analysing the dynamic response curves obtained by numerical simulations between the distorted similar model and prototype. The errors of the maximum displacement and deceleration were 2.70% and 8.26%. The results show that the similitude distortion method is reliable to relate dynamic responses of scaled models to the prototype when the vehicle body thickness distorts.

Migration, movements, and survival in a partially migratory elk (Cervus canadensis) population

AbstractMigration provides an adaptive strategy to improve fitness by allowing individuals to exploit gradients of resources and changes in predation risk. In recent decades, the extent and prevalence of migration has declined in numerous ungulate species including many populations of elk (Cervus canadensis). Resident elk are often more closely associated with human activity, are more readily involved in agricultural conflicts and may contribute to overgrazing on some ranges. We evaluated migratory trends, survival rates, and causes of mortality in a partially migratory elk population in southeastern British Columbia, Canada, and compared these parameters with data from a 1982–1996 study from the same area. Analysis of 201 animal‐years (n = 78 collared cow elk) between 2016 and 2022 showed a ratio of 52% migratory to 48% resident elk, similar to what was found during the 1982–1996 study (55% migratory to 45% resident; n = 40 cows). Among the migrants, 55% were standard migrants (traveling moderate to longer distances and changing little in elevation), 35% elevational, and 10% mixed/atypical. We detected a 14% yearly switching rate between migratory and resident strategies. We recorded 30 mortalities: 47% from human causes, predominantly elk‐vehicle/train collisions (33% of mortalities), apparent starvation/old age (17%), and predation (17%). Notably, while mortality from natural causes was similar between strategies, human‐caused mortality was nearly twice as high in resident elk. Signs of nutritional stress and lower pregnancy rates indicated potential forage limitations. Migrants had higher average survival rates (0.90) compared to residents (0.83), a shift from the 1982–1996 study that recorded higher resident survival rates (0.98), the same migrant survival rate (0.90), and fewer elk‐vehicle/train collisions. Cow elk in our study made fewer and shorter movements into upper mountain tributaries and greater use of mine properties than observed during the 1982–1996 study. Wildlife managers should consider opportunities to enhance elk forage within traditional high‐elevation summer ranges and mitigations to reduce elk‐vehicle/train collisions. Further research is needed to quantify reproductive success, monitor calf survival, and determine relative forage quality among summering areas between migratory strategies, and determine winter ranges of long‐distant migrants.

A Model of Accident Avoiding System for Trains

Abstract: Safety must be the top most priority in train running and it plays an important role in safe running of trains in our country. Already many systems are existed to avoid accidents, even though an attempt is made in this project work to study the subject of train accidents avoiding systems to enhance the technology further. The Train Collision Avoidance System (TCAS) is the major subject to detect unsafe situations arising due to over speed and train collisions in station area. The advanced accident prevention methods must be incorporated those measures under which the trains will be in constant communication with the protection systems through electronic communication systems. Although, Indian Railways have safe running devices, we have made an effort to cover few more important features like, 1 – to detect the track break in running rail track, 2 – object detection in between the tracks with auto control, and 3 – auto stop when train detects Red signal present over the track side signal post. These are the three important parameters incorporated in our project work and for live demo, a mini model train will be constructed over which all required sensors and their control circuits will be installed to prove the theme practically.

Railway Train Collision Avoidance on Same track and Animal Detection Using AI - IoT

The increasing demand for safer and more efficient railway transportation systems has prompted the exploration of advanced technologies to mitigate collision risks and address emerging challenges such as animal incursions on railway tracks. This paper proposes a comprehensive solution leveraging Artificial Intelligence (AI) and Internet of Things (IoT) technologies for real-time detection and avoidance of collisions between trains operating on the same track and encounters with animals. These sensors capture various environmental and operational data such as train positions, velocities, and track conditions. The AI algorithms analyze this data to identify potential collision risks and animal intrusions. Additionally, real-time communication between trains and the centralized control system enables timely intervention and rerouting decisions to ensure safe operations. The system incorporates advanced image recognition algorithms to detect and classify animals near railway tracks. Utilizing high-resolution cameras and IoT-connected devices, the system identifies animals in the vicinity and alerts train operators or initiates automated braking mechanisms to prevent accidents caused by animal incursions. Key features of the proposed system include scalability to accommodate varying railway infrastructures, adaptability to diverse environmental conditions, and interoperability with existing railway signaling and control systems. Moreover, the integration of AI and IoT technologies enhances the system's predictive capabilities, enabling proactive risk mitigation and improving overall.

FOD-YOLO NET: Fasteners fault and object detection in railway tracks using deep yolo network1

Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research. A rapid advancement in deep learning has promoted new possibilities for research in this field. In this work, a novel deep learning-based FOD-YOLO net is proposed for detecting the fasteners faults and objects in the railway tracks. There are two basic components in the deep learning-based YOLOv8: the backbone and the head. YOLOv8 utilizes an improved version of the CSPDarknet53 network for detecting objects on the railway track. The head of YOLOv8 consists of EfficientNet with various convolutional layers with squeeze and excitation blocks for detecting any defect in the track fasteners. These layers are liable for detecting the objectness scores, bounding boxes and class probabilities structured with fully connected layers for the objects and faults in tracks. Based on the results from the Yolo network, the alert message is sent to the loco pilot to avoid accidents using fuzzy logic. The experimental fallouts of proposed FOD-YOLO net achieve higher accuracy and yields better evaluation results with 98.14% accuracy, 98.84% precision and 95.94% recall. From the experimental results, the FOD-YOLO net improves the overall accuracy range by 5.44%, 4.72%, 0.73%, and 13.18% better than Fast RCNN, YOLOv5s-VF, YOLO-GD, and 2D-SSA + Deep network respectively.

Impact force curve feature and failure mode identification method of concrete-filled steel tube members under lateral impact

A collision accident caused by derailed trains is a potential safety hazard for the structure adjacent to high-speed rail lines. Concrete-filled steel tube (CFST) columns are frequently utilized in modern high-speed railway stations, however CFST station columns may suffer severe damage from train collisions because of their high impact energy. Therefore, the method of identifying CFST members' failure modes under large impact energy was investigated in this paper. Adopting numerical simulation by LS-DYNA, the corresponding relationship between the local indentation and impact process was researched, the characteristic impact force time-history curve was proposed, and the effects of impactor and CFST specimen parameters on impact force feature were analyzed. The plateau proportion was established and quantified, then the mapping relationship between plateau proportion and failure degree was acquired. Research results indicated that local indentation developed primarily at impact force plateau stage. CFST specimens showed bending deflection when plateau proportion was below 67.5%, experienced fracture failure when it was above 77%, and exhibited crack failure when it was in between respectively. Finally, the empirical formulas for plateau proportion and maximum deflection were established, and a simplified approach for predicting the failure modes was put forward. The research provides suggestions for identifying damage patterns and strengthening CFST columns subjected to lateral impact loads.

Design optimization of the bamboo-inspired foam-filled tube for high-speed train collision energy absorption

This study first developed a novel bamboo-inspired foam-filled tube (BFFT) for potential application in high-speed trains, considering the practical use conditions. The BFFT was initially designed by mimicking the macro and micro characteristics of bamboo culms and understanding their axial crushing mechanical behavior. The key bionic parameters are drawn based on the crashworthiness indices of natural bamboos with various geometric sizes and growth ages. The numerical simulation model of the BFFT under axial impact load was established and verified using simplified super folding element (SSFE) theory and drop-weight experiments. A multi-objective optimization on the BFFT was conducted to find the optimal parameter alternative, integrating experimental design method of the optimal Latin hypercube, the response surface agent model and the non-dominated sorting genetic algorithm (NSGA-II). The crashworthiness performance of the optimized BFFT was compared with the initial design scheme, a conventional circular tube and other existing energy absorbers of rail vehicles. The results indicated that the EA, SEA and IPCF values of the optimized BFFT can be improved by 150.2 %, 63.3 %, and 48.5 %, respectively, when considering the single target requirement. In comparison to the initial design scheme and conventional circular tube, the SEA value of the final optimized BFFT increased by 44.7 % and 53.0 %, respectively. The proposed novel BFFT demonstrated a superior crashworthiness, providing inspiration for enhancing and optimizing passive safety performance of high-speed trains.

Railway Accident Prevention using Ultrasonic Sensor with Microcontrollers

Railway Transport is indispensable in modern day life, both for business and private users. Nowadays, rail networks across the world are getting busier with trains travelling at higher speeds and carrying more passengers and heavier axle loads than ever before. The combination of these factors has put considerable pressure on the existing infrastructure, leading to increased demands in inspection and maintenance of rail assets. But nowadays, it is not that much safer as lot of accidents occur due to improper communication among the network like wrong signalling , worst weather condition, immediate route change, etc., The train driver doesn‟t get proper information on time and before time so that the hazardous condition can occur. While maritime and air transport are already benefiting from collision avoidance application based on infrastructure less communications. We propose this system to avoid train collision by using Ultrasonic Sensors to provide communication between trains and to avoid same track collisions

Self-adaptive rail edge detection for trams based on mathematical morphology.

With the growing number of tram operation lines, tram-related traffic incidents, particularly train collisions, have become a major issue. Therefore, the ability to identify foreign objects on a track is critical to tram operational safety. Accurately identifying the rail edge is a critical technology for recognizing the track area and providing early warnings of potential threats. Therefore, this study proposes a self-adaptive rail-edge detection algorithm that uses mathematical morphology and computer vision technology to accurately extract rail edges. The performance of the proposed algorithm was compared to that of existing algorithms, including the Canny algorithm and two other methods described in publication. Three scenes in the level crossing area of trams were considered as the research objects, and the effects of two types of noise in the image were explored in terms of the investigated using mean square error (MSE), peak signal-to-noise ratio (PSNR), and computational time. The results showed that the proposed model exhibited strong robustness for different scenes, particularly in the presence of noise. This suggests that the proposed algorithm could be used in early warning systems of trams to identify rail edges.

Passive Safety Assessment of Railroad Trains in Moose Herd Collision Scenarios

Moose herd–train collisions represent one of the potential hazards that railway operations must contend with, making the assessment of passive train safety in such scenarios a crucial concern. This study analyzes the responses of bullet trains colliding with moose herds and investigates the influence of various factors under these conditions. To achieve this goal, a multibody (MB) model was developed using the MADYMO platform. The displacement of the moose’s center of gravity (CG) was employed to assess the safety boundaries, while the relative positions between the wheels and rails were used to evaluate the risk of derailment. The findings revealed that the collision forces exhibited multi-peak characteristics that were subsequently transmitted to the wheel–rail contact system, resulting in disturbances in the relative positions of the wheels and rails. However, these disturbances did not reach a level that would induce train derailment. Furthermore, larger moose herds exhibited higher throw heights, although these heights remained within safe limits and did not pose a threat to overhead lines. The primary safety risk in moose–train collisions stemmed from secondary collisions involving moose that had fallen onto the tracks and oncoming trains. This study offers valuable insights for enhancing the operational safety of high-speed trains and safeguarding wildlife along railway corridors.