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The purpose of this paper is to describe solutions to yet unsolved problems of autonomous vehicles and infrastructure communication via the Internet of Things (IoT). The paper, in the form of a conceptual article, intentionally does not contain research elements, as we plan to conduct simulations in future papers. Each of the many forms of communication between vehicles and infrastructure (V2I) or vice versa offers different possibilities. Here, we describe typical situations and challenges related to the introduction of autonomous vehicles in traffic. An investment in V2I may be necessary to keep the traffic of autonomous vehicles safe, smooth, and energy efficient. Based on the review of existing solutions, we propose several ideas, key elements, algorithms, and hardware. Merely detecting the road infrastructure may not be enough. It is also necessary to consider a new form of travel called the Personal Transporter (PT). The introduction of new systems and solutions offers benefits for both autonomous vehicles and vehicles with a low degree of automation.

The objective of this paper is to design a new system to reduce the risk of derailment at crossings, which are critical points in railway lines. Crossings are a common element in conventional lines of current railway systems and are the only point on the track where there is a discontinuity. Our proposal is based on adding an element to the crossing that occupies part of the crossing gap, providing a larger support surface next to the wing rail, such that the wheel does not fall into the gap. The lateral force—which is the most influential parameter in derailments—is substantially decreased, thus reducing the risk of derailment due to lifting on the rail. The proposed approach also increases the safety of the dynamic behaviour, which has a direct impact on passenger comfort and influences the service life of both the rolling stock and the track, thus reducing the cost and even increasing safety at higher speeds. It has a simple structure that is easy to assemble and does not interrupt traffic during installation. The results of simulations using this innovative solution indicate a significant reduction in lateral stresses and strains on the track, which undoubtedly produces an improvement in traffic safety; however, the results cannot be fully quantified in terms of accident reduction with only the data obtained from simulations. Therefore, it was concluded that implementation of the new crossing design provides better conditions for rolling stock to run on turnouts, increasing safety by reducing the risk of derailment. Nevertheless, it will be necessary to carry out a program of experimental tests, which we intend to make the subject of future research.

Intelligent transport systems (ITSs) rely on wireless communications that provide many services to ground and aerial vehicles. We believe that vehicular communication protocols can evolve the train communication systems into the next generation. However, we found that channel models in train track environments at the 5.9 GHz frequency band are scarcer than in vehicular environments. Therefore, we conduct channel measurements at the 5.86–5.91 GHz ITS band at various railroad crossings in the United States. This allows us to extract the channel parameters and evaluate the propagation channel characteristics. The evaluations show a certain similarity between the train track channel characteristics and the vehicular communications channel characteristics. The railroad channel with an omnidirectional antenna is similar to a suburban environment in the vehicular channel, and with a bidirectional antenna, it is similar to a highway LoS environment in the vehicular channel. However, more importantly, the population of the surrounding buildings and the size of the LoS window can highly affect the RF propagation characteristics.

The Indian Railway network is the world’s fourth largest, transporting millions of people every day. One of the most difficult challenges for travelers is crossing the overhead bridges or subways to reach the right platform. To make this experience more comfortable we have developed the automatic system termed Railway Platform Crossing Automatic Bridge (RPCAB) to connect two opposite platforms. Here, the fabricated metal frame bridge is moved using a pair of double acting hydraulically/ pneumatically actuated telescopic cylinders. After the train pulls out of the station, the bridge connects to the other side of the platform, allowing passengers to walk on the bridge to cross the tracks. The position sensors, alarms, audio/visual indicators, and actuators are all in sync with the train traffic signaling system and the master controller, a Programmable Logic Controller (PLC). To prevent any mishaps from happening, a comprehensive safety interlock system has been implemented, including position sensors, safety barricades, emergency alarms, and an audio-visual information system. The proposed mechanical bridge facilitates the passage for the passengers who are physically impaired, with heavy luggage, pregnant women, and the elderly persons to cross the platform. Additionally, it controls the congestion of passengers when the train has left the station. The proposed system is simulated using PLC simulator for testing, validation, and analysis of the system’s behavior in a simulated environment. The simulation results presented in this paper show how efficient and reliable the proposed design is. Prior to constructing a working prototype in real time, it is essential to put the system through a virtual environment. The results support the viability of applying the proposed design in real-world settings, which will improve both safety and efficiency at railway platform crossings.

Railroad-highway grade crossings (RHGCs) are critical points of conflict between roadway and rail systems, contributing to over 2000 crashes and 250 fatalities annually in the United States. This study applied machine learning methods (ML) techniques to model and predict crash frequency at RHGCs, using a comprehensive dataset from the Federal Railroad Administration (FRA) and Tennessee Department of Transportation (TDOT). The dataset included 807 validated crossings, incorporating roadway geometry, traffic volumes, rail characteristics, and control features. Five ML models—Random Forest, XGBoost, PSO-Elastic Net, Transformer-CNN, and Autoencoder-MLP—were developed and compared to a traditional Negative Binomial (NB) regression model. Results showed that ML models significantly outperformed the NB model in predictive accuracy, with the Transformer-CNN achieving the lowest Mean Squared Error (21.4) and Mean Absolute Error (3.2). Feature importance analysis using SHAP values consistently identified Annual Average Daily Traffic (AADT), Truck Traffic Percentage, and Number of Lanes as the most influential predictors, findings that were underrepresented or statistically insignificant in the NB model. Notably, the NB model failed to detect the nonlinear relationships and interaction effects that ML algorithms captured effectively. While only three variables were statistically significant in the NB model, ML models revealed a broader spectrum of critical crash determinants, offering deeper interpretability and higher sensitivity. These findings emphasize the superiority of machine learning approaches in modeling RHGC safety and highlight their potential to support data-driven interventions and policy decisions for reducing crash risks at grade crossings.

PurposeRail level crossing removals to improve transport performance across metropolitan Melbourne (state of Victoria) resulted in new rail fencing and grade-separation of tracks from the surrounding environment at several sites. These design changes restricted pedestrian access to the rail tracks, which is a countermeasure known to prevent railway suicide in other settings. We examined whether any such suicide prevention effect followed the removals.MethodsWe used a multiple-arm pre-post design to test whether a decrease in monthly frequency of railway suicides occurred at level crossing removal sites (intervention sites), compared to randomly matched sites where level crossings had not yet been removed (control sites). We used data available in the Victorian Suicide Register covering the period 1st January 2008 to 30th June 2021.ResultsThe mean monthly number of railway suicides decreased by 68% within a 500 m radius of intervention sites (RR: 0.32; CI 95% 0.11–0.74) and by 61% within a 1000 m radius of intervention sites (RR: 0.39; CI 95% 0.21–0.68). There was no evidence that the mean monthly number of railway suicides changed at the control sites, either within a 500 m radius (RR: 0.88; CI 95% 0.47–1.56) or a 1000 m radius (RR: 0.82; CI 95% 0.52–1.26).ConclusionThe reduction in railway suicides at locations where level crossings were removed, demonstrates the suicide prevention benefits that can be derived from a major infrastructure project even if not initially intended. Planning for major infrastructure projects should include consideration of these benefits, with designs incorporating features to maximise suicide prevention impact.

Object detection at coal transportation railway crossings is crucial for accident prevention and traffic efficiency improvement. However, the application of existing methods on resource-constrained devices has seldom been considered. To address these challenges, in this paper, we propose a lightweight railway crossing object detection algorithm based on the Transformer framework, referred to as Light-Weight DEtection TRansformer (LW-DETR). In this algorithm, the Paddle Paddle-Lightweight CPU Convolutional Network (PP-LCNet) is employed as the backbone network, where standard convolution is combined with depthwise separable convolution for multi-scale feature extraction. Furthermore, the cross-scale feature fusion module is optimized to reduce redundant calculations and enhance feature fusion efficiency. Moreover, the Scylla-Intersection over Union loss function is introduced to comprehensively evaluate bounding box similarity, thereby improving object detection accuracy. Ablation experiments conducted on a modified Pascal Visual Object Classes (Pascal VOC) dataset demonstrate that LW-DETR, while maintaining acceptable detection accuracy, achieves a 135.3% increase in frames per second, a 71.7% reduction in parameters, and a 73.7% decrease in computational load, leading to effective lightweight performance. Comparative experiments with other popular object detection algorithms further confirm that LW-DETR significantly enhances detection speed while maintaining high accuracy, considerably reducing model size and validating the effectiveness of these improvements.

Multiple projects within the rail industry across different regions have been initiated to address the issue of over-population. These expansion plans and upgrade of technologies increases the number of intersections, junctions, and level crossings. A level crossing is where a railway line is crossed by a road or right of way on the level without the use of a tunnel or bridge. Level crossings still pose a significant risk to the public, which often leads to serious accidents between rail, road, and footpath users and the risk is dependent on their unpredictable behavior. For Great Britain, there were three fatalities and 385 near misses at level crossings in 2015–2016. Furthermore, in its annual safety report, the Rail Safety and Standards Board (RSSB) highlighted the risk of incidents at level crossings during 2016/17 with a further six fatalities at level crossings including four pedestrians and two road vehicles. The relevant authorities have suggested an upgrade of the existing sensing system and the integration of new novel technology at level crossings. The present work addresses this key issue and discusses the current sensing systems along with the relevant algorithms used for post-processing the information. The given information is adequate for a manual operator to make a decision or start an automated operational cycle. Traditional sensors have certain limitations and are often installed as a “single sensor”. The single sensor does not provide sufficient information; hence another sensor is required. The algorithms integrated with these sensing systems rely on the traditional approach, where background pixels are compared with new pixels. Such an approach is not effective in a dynamic and complex environment. The proposed model integrates deep learning technology with the current Vision system (e.g., CCTV to detect and localize an object at a level crossing). The proposed sensing system should be able to detect and localize particular objects (e.g., pedestrians, bicycles, and vehicles at level crossing areas.) The radar system is also discussed for a “two out of two” logic interlocking system in case of fail-mechanism. Different techniques to train a deep learning model are discussed along with their respective results. The model achieved an accuracy of about 88% from the MobileNet model for classification and a loss metric of 0.092 for object detection. Some related future work is also discussed.

Traffic congestion is a perpetual problem for the sustainability of transportation development. Traffic congestion causes delays, inconvenience, and economic losses to drivers, as well as air pollution. Identification and quantification of traffic congestion are crucial for decision-makers to initiate mitigation strategies to improve the overall transportation system’s sustainability. In this paper, the currently available measures are detailed and compared by implementing them on a daily and weekly traffic historical dataset. The results showed each measure showed significant variations in congestion states while indicating a similar congestion trend. The advantages and disadvantages of each measure are identified from the data analysis. This study summarizes the current road traffic congestion measures and provides a constructive insight into the development of a sustainable and resilient traffic management system.

Point machines are the actuators for railway switching and crossing systems that guide trains from one track to another. Hence, the safe and reliable behavior of point machines are pivotal for rail transportation. Recently, scholars and researchers have attempted to deploy various kinds of sensors on point machines for anomaly detection and/or incipient fault detection using date-driven algorithms. However, challenges arise when deploying condition monitoring and fault detection to trackside point machines in practical applications. This article begins by reviewing studies on fault and anomaly detection in point machines, encompassing employed methods and evaluation metrics. It subsequently conducts an in-depth analysis of point machines and outlines the envisioned intelligent fault detection system. Finally, it presents eight challenges and promising research directions along with a blueprint for intelligent point machine fault detection.