Search Results (33)

Railway traffic monitoring requires robust detection technologies capable of operating reliably under real-world vibration and environmental conditions. In this work, we present the design and validation of an optical vibration sensor based on a Single-mode–Multimode–Single-mode (SMS) fiber structure for Light Rail Vehicle (LRV) detection. The sensing mechanism relies on multimodal interference in the multimode fiber (MMF), where rail-induced vibrations modify the guided mode distribution and, consequently, the transmitted optical intensity. The optical signal is converted to voltage and processed through an embedded acquisition system. Additionally, we conducted tests with freight trains and maintenance trains in order to evaluate the applicability of the sensor in other types of trains besides the LRV. We conducted laboratory experiments to assess mechanical stability, sensibility, and packaging strategies, followed by supervised field tests on an operational LRV line. The recorded time-domain signal exhibited clear modulation during train passage, and first-derivative and sliding-window variance analyses were applied to reliably identify vibration events, even in the presence of slow baseline drift. In addition, frequency-domain analysis was performed by applying the Fast Fourier Transform (FFT) to the measured signal, enabling the identification of characteristic low-frequency spectral components induced by train passage. A quantitative sensitivity assessment was further carried out by correlating the integrated spectral energy (0–12 Hz) with vehicle weight, yielding a linear response with a sensitivity of 0.0017 a.u./t and coefficient of determination $R^2=0.933$. The proposed solution demonstrated stable operation using commercially available low-cost components, confirming the feasibility of SMS-based optical sensing for railway monitoring. These results indicate strong potential for future deployment in traffic safety systems and distributed sensing networks. Full article

Foreign object intrusion in railway tracks is a major threat to train operation safety, yet current detection methods face challenges in identifying small distant targets and adapting to low-light conditions. Moreover, existing systems often lack the ability to assess intrusion risk levels, limiting real-time warning and graded response capabilities. To address these gaps, this paper proposes a novel method for intrusion detection and risk assessment based on keypoint spatial discrimination. First, an XS-BiSeNetV2-based track segmentation network is developed, incorporating cross-feature fusion and spatial feature recalibration to improve track extraction accuracy in complex scenes. Second, an enhanced STI-YOLO detection model is introduced, integrating a Shuffle attention mechanism for better feature interaction, a high-resolution Transformer detection head to improve small-target sensitivity, and the Inner-IoU loss function to refine bounding box regression. Detected targets’ bottom keypoints are then analyzed relative to track boundaries to determine intrusion direction. By combining lateral distance and motion state features, a multi-level risk classification system is established for quantitative threat assessment. Experiments on the RailSem19 and GN-rail-Object datasets show that the method achieves a track segmentation mIoU of 88.19% and a detection mAP of 82.6%. The risk assessment module effectively quantifies threats across scenarios and maintains stable performance under low-light and strong-glare conditions. This work offers a quantifiable risk assessment solution for intelligent railway safety systems. Full article

In this study, we propose a novel model based on multi-source spatio-temporal features and optimized ensemble learning for forecasting station- and line-level passenger flows of urban rail transit. First, we design a spatio-temporal feature engineering method to enhance the accuracy of forecasting using passenger flow features; the temporal features include periodic and lag effects and the spatial features cover spatio-temporal attention mechanisms, adjacency relationships in the network graph and station clustering features. Furthermore, an improved ensemble learning method based on Extra Randomized Trees (ExtraTrees) and Light Gradient Boosting Machine (LightGBM) is developed to forecast the station-level passenger flows using a weighted sum method in which a particle swarm optimization algorithm is adopted to determine the weights assigned to the forecasting results of the two models. Finally, ridge regression is adopted as the meta-learning model to forecast line-level passenger flows. We employed passenger flow data from three urban rail transit lines in Hangzhou to demonstrate the feasibility of the proposed model. The results indicate that it produces more accurate passenger flow forecasts at the station and line levels than benchmark models. Therefore, it can provide a solid support for optimizing the operations, management, and planning for both a single urban rail transit station and the entire network. Full article

With the growing focus on sustainable development, light rail transit (LRT) systems are increasingly viewed as key drivers of low-carbon mobility and spatial equity. However, as urban spatial structures become more stable, it remains unclear whether LRT systems can still enhance quality of life, property values and contribute to inclusive urban regeneration. This study explores Manchester, Sheffield, and Nottingham, three UK cities with recent LRT development experience, as case studies. Using LRT constructed or expanded between 1995 and 2019 as a quasi-natural experiment, a difference-in-differences (DID) model is applied to estimate the causal impact of LRT expansion on property prices. The results indicate that LRT construction can lead to a 4.44% to 8.29% increase in nearby property values, with a lagged effect observed after implementation. The impact is more pronounced in areas with well-developed bus networks and in lower-income areas. Further mechanism analysis suggests that the effect is indirectly driven by improved accessibility and enhanced convenience of access to local amenities. Full article

Ensuring accessibility in urban public spaces is a key challenge for contemporary cities, particularly in the context of ageing populations, socio-spatial inequalities, and the global call for inclusive urban development. Despite its importance, accessibility is often treated as a cross-cutting issue rather than as a central objective in planning practice. This study examines how accessibility can be addressed in participatory urban public space design through a geodesign workshop conducted with architecture students from the University of Alicante. Focusing on the area along Line 2 of the TRAM light-rail network in Alicante, Spain, the workshop applied the geodesign framework in four iterative phases: system analysis, stakeholder role-play, design negotiation, and consensus building. The workshop participants represented six stakeholder groups with varying objectives and priorities, proposing micro-interventions in vulnerable urban areas aimed at improving walkability, surface conditions, and access to services. The role-play phase highlighted contrasting views on accessibility, particularly emphasised by groups representing older adults and people with disabilities. Negotiation revealed both alliances and tensions, while the final consensus reflected a moderate but meaningful inclusion of wide accessibility concerns. The resulting proposals showed spatial awareness of socio-territorial inequalities. The findings suggest that geodesign fosters critical thinking, collaboration, and empathy in future urban professionals; however, challenges persist regarding inclusivity, contextual adaptation, and integration into practice. Future work should explore long-term impacts and co-creation of accessibility standards. Full article

Urban rail transit (URT) systems are essential to ensuring efficient and sustainable urban mobility. However, the core components of operational planning, service frequency setting, train timetabling, and train allocation are often optimized separately, leading to fragmented decision-making and suboptimal system performance. This study addresses that gap by proposing an integrated optimization framework that simultaneously considers all three planning layers under time-dependent passenger demand conditions. The problem is formulated as a bi-objective Integer Nonlinear Programming (INLP) model, aiming to jointly minimize passenger waiting time and total operational cost. To solve this large-scale, combinatorial problem, a tailored Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed. The algorithm incorporates discrete variable handling, constraint-preserving mechanisms, and a customized encoding scheme that aligns with the structural characteristics of URT operations. The proposed framework is applied to real-world data from the Addis Ababa Light Rail Transit (AALRT) system. The results demonstrate that the MOPSO-based approach offers a more diverse and operationally feasible set of trade-off solutions compared to a widely used benchmark algorithm, NSGA-II. Specifically, it provides transit planners with a flexible decision-support tool capable of identifying schedules that balance service quality and cost, based on varying strategic or budgetary priorities. By integrating interdependent planning decisions into a unified model and leveraging the strengths of a customized metaheuristic algorithm, this study contributes a scalable, adaptable, and practically relevant methodology for improving the performance of urban rail systems. Full article

With accelerating urbanization, the development of rail transit systems—particularly subways—has become a key strategy for alleviating urban traffic congestion. However, existing studies on subway station site selection often lack a spatially continuous evaluation of site suitability across the entire study area. This may lead to a disconnect between planning and actual demand, resulting in issues such as “overbuilt infrastructure” or the “island effect.” To address this issue, this study selects Kunming City, China, as the study area, employs the K-means++ algorithm to cluster existing subway stations based on passenger flow, integrates multi-source spatial data, applies a random forest algorithm for optimal positive sample selection and driving factor identification, and subsequently uses a LightGBM-SHAP explainable machine learning framework to develop a predictive model for station location based on mathematical modeling. The main findings of the study are as follows: (1) Using the random forest model, 20 key drivers influencing site selection were identified. SHAP analysis revealed that the top five contributing factors were connectivity, nighttime lighting, road network density, transportation service, and residence density. Among these, transportation-related factors accounted for three out of five and emerged as the primary determinants of subway station site selection. (2) The site selection prediction model exhibited strong performance, achieving an R² value of 0.95 on the test set and an average R² of 0.79 during spatial 5-fold cross-validation, indicating high model reliability. The spatial distribution of predicted suitability indicated that the core urban area within the Second Ring Road exhibited the highest suitability, with suitability gradually declining toward the periphery. High-suitability areas outside the Third Ring Road in suburban regions were primarily aligned along existing subway lines. (3) The cumulative predicted probability within a 300 m buffer zone around each station was positively correlated with passenger flow levels. Overlaying the predicted results with current station locations revealed strong spatial consistency, indicating that the model outputs closely align with the actual spatial layout and passenger usage intensity of existing stations. These findings provide valuable decision-making support for optimizing subway station layouts and planning future transportation infrastructure, offering both theoretical and practical significance for data-driven site selection. Full article

This study examines travel mode choice behavior within the context of Thailand’s emerging high-speed rail (HSR) development. It conducts a comparative assessment of predictive capabilities between the conventional Multinomial Logit (MNL) framework and advanced data-driven methodologies, including gradient boosting algorithms (Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting) and neural network architectures (Deep Neural Network, Convolutional Neural Network). The analysis leverages stated preference (SP) data and employs Bayesian optimization in conjunction with a stratified 10-fold cross-validation scheme to ensure model robustness. CatBoost emerges as the top-performing model (area under the curve = 0.9113; accuracy = 0.7557), highlighting travel cost, service frequency, and waiting time as the most influential determinants. These findings underscore the effectiveness of machine learning approaches in capturing complex behavioral patterns, providing empirical evidence to guide high-speed rail policy development in low- and middle-income countries. Practical implications include optimizing fare structures, enhancing service quality, and improving station accessibility to support sustainable adoption. Full article

Rail surface defects pose significant risks to the operational efficiency and safety of industrial equipment. Traditional visual defect detection methods typically rely on high-quality RGB images; however, they struggle in low-light conditions due to small, low-contrast defects that blend into complex backgrounds. Therefore, this paper proposes a novel defect segmentation method leveraging a dual-stream feature fusion network that combines polarization images with DeepLabV3+. The approach utilizes the pruned MobileNetV3 as the backbone network, incorporating a coordinate attention mechanism for feature extraction. This reduces the number of model parameters and enhances computational efficiency. The dual-stream module implements cascade and addition strategies to effectively merge shallow and deep features from both the original and polarization images. This enhances the detection of low-contrast defects in complex backgrounds. Furthermore, the CBAM is integrated into the decoding area to refine feature fusion and mitigate the issue of missing small-target defects. Experimental results demonstrate that the enhanced DeepLabV3+ model outperforms existing models such as U-Net, PSPNet, and the original DeepLabV3+ in terms of MIoU and MPA metrics, achieving 73.00% and 80.59%, respectively. The comprehensive detection accuracy reaches 97.82%, meeting the demanding requirements for effective rail surface defect detection. Full article

As urbanization continues to expand, the design and structure of urban spaces increasingly influence the experiences of individuals, whether intentionally or inadvertently. These effects can result in both positive and negative experiences, with urban facilities generally designed to enhance the comfort and well-being of citizens. However, in certain cases, these spaces can provoke adverse emotional reactions, such as anxiety. Anxiety, a prevalent mental health disorder, is more commonly observed in urban environments than in rural areas. Among various urban settings, rail transport in large cities is often cited as one of the most stressful environments for passengers. In light of the significance of this issue, this study seeks to explore how physical and perceptual components can reduce anxiety and encourage greater use of intra-urban rail transportation. Utilizing a qualitative research approach, the study employed directional content analysis to investigate this topic. Data were collected and analyzed through an exploratory methodology with the assistance of MAXQDA software. The analysis began with guided content coding, drawing on theoretical frameworks pertinent to the research. Through this process, 2387 initial codes were identified, which were then categorized into nine main themes, with the relationships between these codes clarified. The findings were inductively derived from the raw data, leading to the development of a foundational theoretical framework. The study, employing a personalized strategy, identified three key factors that contribute to anxiety: physical, perceptual, and environmental components. Physical factors, such as accessibility, lighting, and signage, were found to have a significant impact on passengers’ psychological well-being. Perceptual factors, including personal perceptions, stress, and fear, played a crucial role in exacerbating anxiety. Additionally, environmental factors, particularly the design of metro networks, rail lines, and flexible transportation lines, such as car-sharing and micromobility, were found to significantly contribute to the overall anxiety experienced by passengers. Moreover, the study suggests that anxiety triggers can be mitigated effectively through the implementation of well-designed policies and management practices. Enhancing the sense of security within transit spaces was found to increase citizens’ willingness to utilize rail transportation. These findings indicate that targeted interventions aimed at improving both the physical and perceptual aspects of the transit environment could enhance the commuter experience and, in turn, foster greater use of rail systems. Full article

Rail travel is one of the safest means of transportation, with increasing usage in recent years. One of the major safety concerns in the railway relates to intrusions. Therefore, the timely detection of obstacles is crucial for ensuring operational safety. This is a complex problem with multiple contributing factors, from environmental to psychological. While machine learning (ML) has proven effective in related applications, such as autonomous road-based driving, the railway sector faces unique challenges due to limited image data availability and difficult data acquisition, hindering the applicability of conventional ML methods. To mitigate this, the present study proposes a novel framework leveraging LiDAR technology (Light Detection and Ranging) and previous knowledge to address these data scarcity limitations and enhance obstacle detection capabilities on railways. The proposed framework combines the strengths of long-range LiDAR (capable of detecting obstacles up to 500 m away) and GNSS data, which results in precise coordinates that accurately describe the train’s position relative to any obstacles. Using a data fusion approach, pre-existing knowledge about the track topography is incorporated into the LiDAR data processing pipeline in conjunction with the DBSCAN clustering algorithm to identify and classify potential obstacles based on point cloud density patterns. This step effectively segregates potential obstacles from background noise and track structures. The proposed framework was tested within the operational environment of a CP 2600-2620 series locomotive in a short section of the Contumil-Leixões line. This real-world testing scenario allowed the evaluation of the framework’s effectiveness under realistic operating conditions. The unique advantages of this approach relate to its effectiveness in tackling data scarcity, which is often an issue for other methods, in a way that enhances obstacle detection in railway operations and may lead to significant improvements in safety and operational efficiency within railway networks. Full article

In the realm of road–rail dual-use bridges, conducting accurate vehicle–bridge coupling analysis is crucial, as the combined effects of road traffic and rail transit induce complex dynamic challenges. This study investigates a road–rail dual-use network arch bridge, highlighting the dynamic effects induced by light rail loadings. By employing a noniterative vehicle–bridge coupling analysis method, the dynamic responses of hangers caused by vehicular and light rail loads are effectively captured. Additionally, this study explores the influence of various parameters, including vehicle types, driving lanes, and road surface roughness on the responses of hangers positioned at different locations along the bridge. The findings reveal that light rail induces significantly larger dynamic effects compared to motor vehicles. When the light rail operates closer to the hanger, the responses of hangers are more pronounced. Furthermore, different road surface roughness level notably affects the amplitude of axial stress and bending moment fluctuations. Poorer road conditions amplify these dynamic effects, leading to increased stress variations. These insights underscore the necessity of integrating considerations for both road and rail traffic in the structural analysis and design of network arch bridges to ensure their reliability and serviceability. Full article

The agglomeration railway networks form the backbone of modern urban transport systems, providing safe and reliable access from home to work or school for thousands of residents of agglomeration districts. This article examines the possibilities and directions of development for the agglomeration railway of the city of Poznań, providing a comparative analysis of this system with the networks of the cities of Szczecin and Gdańsk. Each rail system was described and presented in terms of its most important features. The collected data were then collected in tabular form and based on them, a comparison was made using two methods: AHP and COPRAS. Both methods, although with different strengths, indicated the unquestionable advantage of the agglomeration railway in Gdańsk for the adopted assumptions. The Poznań network obtained the weakest result in light of the assumptions. The analysis showed aspects of passenger transport, the improvement of which is crucial for the development of public transport in Poznań, e.g., too low frequency of trains, the need to increase passengers’ awareness of the possibilities of using rail transport, or the need to create stops ensuring a smooth possibility of changing to another means of transport. Full article

Hazardous chemicals are transported on rail and road networks. In the case of accidental spillage or terror attack, civilian and military first responders must approach the scene equipped with appropriate personal protective equipment. The plausible manufacturing of chemical protective polymer material, from photocatalyst anatase titanium dioxide (TiO₂) doped low-density polyethylene (LDPE), for cost-effective durable lightweight protective garments against toxic chemicals such as 2-chloroethyl ethyl sulphide (CEES) was investigated. The photocatalytic effects on the physico-chemical properties, before and after ultraviolet (UV) light exposure were evaluated. TiO₂ (0, 5, 10, 15% wt) doped LDPE films were extruded and characterized by SEM-EDX, TEM, tensile tester, DSC-TGA and permeation studies before and after exposure to UV light, respectively. Results revealed that tensile strength and thermal analysis showed an increasing shift, whilst CEES permeation times responded oppositely with a significant decrease from 127 min to 84 min due to the degradation of the polymer matrix for neat LDPE, before and after UV exposure. The TiO₂-doped films showed an increasing shift in results obtained for physical properties as the doping concentration increased, before and after UV exposure. Relating to chemical properties, the trend was the inverse of the physical properties. The 15% TiO₂-doped film showed improved permeation times only when the photocatalytic TiO₂ was activated. However, 5% TiO₂-doped film exceptionally maintained better permeation times before and after UV exposure demonstrating better resistance against CEES permeation. Full article

Over the last two decades, the United Arab Emirates (UAE) has experienced significant urban growth, prompting the Dubai Roads and Transport Authority (RTA) to advocate for sustainable transport solutions. This led to the implementation of the Light Rail Transit (LRT) to address urban mobility, environmental sustainability, and energy efficiency. Dubai has strategically prioritized infrastructure and transportation network expansion to support its rapid development. This paper aims to examine the critical role of the LRT system, particularly the metro and tramway, in steering Dubai towards sustainability. Metro and tramway systems offer crucial high-capacity public transport, enhance connectivity, stimulate economic growth, and contribute to a sustainable environment. The study assesses the transformative impact of the Dubai Metro on urban development, focusing on key stations like Jabal Ali, Al-Barsha First, and Business Bay. Using qualitative research methods, including GIS, spatial maps, interviews, case studies, and land use investigations, the research analyzes population density, connectivity, accessibility, and urban land use patterns around these stations. Results indicate a positive impact of the Dubai Metro on both commercial and residential land use, improved connectivity, and enhanced accessibility, reinforcing its role in cultivating a sustainable urban environment. Full article