Runway bridges offer a practical structural solution for airport construction in terrain-constrained areas. However, their dynamic behavior under aircraft landing loads remains insufficiently studied, and design-oriented theoretical guidance is lacking. This study presents a novel analytical-numerical model for quantifying the dynamic load allowance (DLA) in runway bridges subjected to aircraft landing. The aircraft is modeled as a spatial six-degree-of-freedom system comprising three mass-spring-damper units, accounting for both pitch and roll motions. The runway bridge is represented by a spatial multi-beam Timoshenko beam system, where lateral coupling between beams is considered to reflect the lateral distribution of the main landing gears. An analytical model is established to quantify the midspan deflection and bending moment DLAs of the central beam in response to aircraft landing, accounting for multi-stage gear contact and aircraft-bridge dynamic coupling. The finite element method is adopted to solve the coupled model, and model validation is performed using field-measured quick access recorder (QAR) data. Parametric analyses indicate that vertical landing velocity and roll angle are the most influential factors governing DLA. Increasing vertical velocity from 0.5 to 2.5 m/s resulted in more than 260% growth in deflection DLA for short-span bridges, while a surface roughness transition from grade C to D amplified DLA by up to 78%. Roll angle induced asymmetric loading, raising deflection DLA by as much as 320%, particularly in shorter spans. In contrast, pitch angle reduced structural response, with deflection DLA decreasing by up to 20%. Horizontal speed exhibited a moderate, span-dependent influence. Roll angle and landing velocity are the main factors affecting DLAs, while horizontal speed has a moderate influence and other factors have minor effects under typical landing conditions. These results highlight the critical role of aircraft dynamics and surface conditions in determining dynamic amplification and confirm the effectiveness of the proposed model in capturing span-sensitive behavior.
Road weather information systems (RWIS) have long been favoured by many North American and European highway authorities as they provide real-time and near-future road condition information during the winter months. Information disseminated by individual RWIS stations is collectively used to efficiently mobilize winter road maintenance operations, promoting safe travel during adverse weather events. However, the high installation and operational costs of RWIS oblige transportation authorities to quantify their cost-effectiveness, particularly in improving traffic safety. Therefore, this study addresses the research question “has RWIS implementation led to a significant reduction in winter weather collisions, making roads safer for commuters?” by implementing one of the most widely adopted methods used in traffic safety studies, the before-and-after empirical Bayes approach. For the evaluation, geographic information science (GIS)-based techniques were used to prepare intensive datasets. Further, safety performance function and yearly calibration factors were locally calibrated using this large-scale spatial data, where network-based service area analysis using GIS played a crucial role in selecting treatment and reference sites. The case study results of seven stations in Iowa, U.S., showed inclement winter collision reduction ranging from 31.53% to 88.23%. This study further answered another research question, “is RWIS a cost-effective safety countermeasure?” by conducting a detailed economic analysis, which estimated benefit-cost ratios ranging from 7.51 to 34.16. The results indicate that RWIS is an economically viable safety countermeasure.
Pavement texture detection is an important means of evaluating road safety performance. Compared with static scanners, vehicle-mounted scanners are widely used because of their high inspection efficiency. However, two major challenges remain in practical applications: (i) a trade-off between testing speed and measurement accuracy, and (ii) limited adaptability to pavements with different brightness levels under varying illumination conditions. Under high-speed scanning, the exposure time must be shortened to maintain a small longitudinal sampling interval, which reduces the reflected laser energy captured by the camera during each profile acquisition. This problem becomes more severe for low-reflectivity dark asphalt pavements, leading to insufficient returned laser intensity, invalid profile points, and increased measurement errors. To address this issue at the data-acquisition stage, a high-precision vehicle-mounted laser scanning system (HVLS) equipped with a supplemental lighting device was developed in this study. The supplemental lighting device provides additional 405 nm illumination aligned with the built-in laser line, thereby enhancing the reflected laser signal under short-exposure conditions. This system meets the sampling requirements for macroscopic texture in the direction of travel across a wavelength range of 0.5–50 mm. It improves high-speed pavement macro-texture measurement with a longitudinal sampling interval of 0.2 mm and increases the scanning speed by up to 300% compared with the HVLS without supplemental lighting, without compromising the accuracy of MPD, Ra, and RMS measurements. Through field tests conducted on pavements with different brightness levels under varying illumination conditions, adaptive photosensitive parameters were determined, enabling HVLS to meet multi-scenario pavement texture detection requirements.
Individual travel behaviors demonstrate a degree of regularity, particularly concerning time, space, and frequency repetition. Measuring the regularity of individual travel behavior contributes to understanding variations among individuals and provides opportunities for personalized transportation services. However, previous measurement methods have not comprehensively addressed these three aspects simultaneously or sufficiently captured the repetition of “basic travel behaviors”, while also relying on diverse data types without a comprehensive methodology covering multiple datasets. To address these research gaps, this paper presents an innovative method for measuring the regularity of individual users, which considers the repetition of basic travel behaviors along with temporal, spatial, and frequency dimensions. Applying this method to historical trip data from bike-sharing and subway systems has illustrated the effectiveness and applicability of the approach proposed in this paper. The findings suggest the broad applicability of this method across various data types, provided that the data allows for the extraction of information regarding users' travel origins and destinations. Moreover, this method effectively produces a regularity value to assess and compare the overall regularity of users, surpassing previous limitations that solely relied on using frequency as an initial filter or distinguishing users as regular or irregular based solely on frequency thresholds.
In recent scholarly discourse, it has been noted that connected and automated vehicles (CAVs) have offered substantial developmental potential and a variety of implementation possibilities for the refinement and advancement of adaptive traffic signal control (ATSC). However, as the market penetration rate of CAVs is still relatively low, it is essential to consider the use of existing, cost-effective detectors that can form an integral component of the ATSC system. Additionally, it is crucial to consider drivers' behaviours in the context of emerging mixed traffic environments in order to reflect the realities of traffic flow in simulations due to the distinction between human-driven vehicles and CAVs. To address these issues, an ATSC algorithm was proposed to optimize signal timing to improve safety and operational performance at isolated intersections. The proposed algorithm leveraged real-time Q-learning with loop detector and CAV data to obtain optimized green time, while a driven-behaviour model was introduced to describe human factors in mixed traffic environments. Numerical studies were conducted using simulation of urban mobility (SUMO) to evaluate algorithm performance and investigate the influence of different factors. The results indicate that the proposed algorithm has significant practical value in simultaneously improving safety with a demonstrated reduction in the conflict rate ranging from 28.6% to 72.7% and operational efficiency with a drop in the waiting time ranging from 12.6% to 61.4%, compared to traffic-actuated control. Moreover, it is low-cost and adaptable, and can be continuously updated with real-time driving data while also serving as a layer in next-generation high-definition maps.
The state of the road surface weather is crucial to road safety, and road icing is one of the main factors causing traffic accidents. To promote the development of road icing recognition and sensing technology, this article reviews the research progress of road icing sensors both domestically and internationally. Firstly, the classification of pavement status was introduced, the hazards of pavement icing to traffic safety were clarified, and the importance of pavement icing identification to traffic safety management was recognized. Then, based on the different ways of pavement icing perception, the pavement icing sensor was divided into contact pavement icing sensor and non-contact pavement icing sensor, and the classification, working principle and characteristics of pavement icing sensor were analyzed and summarized. At the same time, based on the differences of different pavement state recognition technologies and the actual road inspection and monitoring needs, the application scope, recommendation index and improvement suggestions of various pavement icing sensors are given. Finally, the future improvement direction of pavement icing sensor is discussed. It is suggested that the meteorological information system of the road network should be combined with the road icing sensor to realize the timely warning of the road icing state and improve the road traffic safety.
Rapid and reliable route planning is of paramount significance for first responders to respond to emergent situations promptly, hence minimizing damages and casualties. This paper presents a new graphics processing unit (GPU) enabled evolutionary dynamic programming (EDP) algorithm designed for rapid multi-source route and rescue planning, addressing the urgent need for real-time decision-making for first responders. It considers potential delays caused by unexpected railroad crossing blockages in densely populated metropolitan areas by incorporating real-time traffic information to identify an optimal route with the shortest response time during emergencies. Specifically, the EDP allows effective utilization of massive GPU computing threads for rapid and accurate pathfinding subject to train blockage constraints. A new method for GPU resource allocation at the structural level is also proposed, which constructs GPU threads as two-dimensional blocks to enable efficient route computation between a starting node and any nodes within the road network. The performance of our method is validated through case studies involving multiple emergency scenarios in the City of Columbia, SC. The results demonstrate that the method can find the optimal route with train blockage constraints within 1 s, a significant improvement over our prior method. This research has the potential to significantly enhance emergency response efficiency, enabling first responders to navigate urban environments with unprecedented speed and reliability.
To address the large unbalanced cable forces in conventional saddle-pylon systems of long-span suspension bridges, a main-cable-force self-propelled system and an innovative self-propelled saddle equipped with a row of rollers are proposed. Compared with traditional anti-slip saddle structures, the proposed system converts sliding friction into rolling friction and enables the self-balancing transmission of horizontal cable forces. However, the introduction of rollers significantly changes the load-transfer mechanism and may result in severe local contact stress concentrations, which could affect the service life and operational performance of the saddle structure. In this study, the newly developed self-propelled saddle was taken as a representative engineering case. A three-dimensional finite element model was established to investigate the contact stress distribution characteristics and was further verified using Hertz contact theory. The contact stress states of the rollers, saddle base, and pylon top surface were analyzed in detail. The results show that the contact stress distribution is highly non-uniform along the axial direction. The side rollers experience significantly higher contact stresses than the middle rollers, while the maximum stresses are concentrated near the edge regions of the contact surfaces. The observed stress concentration is mainly attributed to the fan-shaped upper saddle geometry and the thickness and inclination of the reinforcing ribs. Based on the identified stress-transfer characteristics, optimization measures concerning structural configuration and material selection are proposed to improve stress distribution and reduce local stress concentration. The proposed analysis framework and optimization strategy provide useful guidance for the design, assessment, and practical application of innovative self-propelled saddle systems in future ultra-long-span suspension bridges.
To enhance the usage performance of asphalt pavement under extreme climate temperatures, the heat insulation performance of asphalt pavement bonding layer was endowed, and the mechanical properties, bonding performance, and durability of heat insulation bonding layer materials (HIBLM) were revealed. A method for evaluating the heat preservation effect was designed, and the heat preservation performance of HIBLM was compared and evaluated. The results show that compared with hollow glass beads (HGB)-HIBLM and expanded perlite (EP)-HIBLM, the mechanical properties of floating beads (FB)-HIBLM are outstanding, and the 7 d tensile strength can be increased by 6%–12%. The bonding strength of FB-HIBLM and EP-HIBLM is the best, which can increase by 23%–28% compared to HGB-HIBLM. After 30 d temperature change cycles or 15 d immersion in water, the mechanical and interlayer bonding properties of HGB-HIBLM, FB-HIBLM, and EP-HIBLM decrease less than those of ordinary emulsified asphalt (OEA), showing excellent durability. In the 100 min cooling test, the average cooling rate of the lower surface of the HIBLM decreased by 17%–18%. At 85–100 min, the maximum heat preservation temperature differences of HGB-HIBLM, FB-HIBLM, and EP-HIBLM are 4.8 °C, 4.7 °C and 4.7 °C, respectively, and the heat preservation values are 2.5 °C, 2.3 °C and 2.2 °C, respectively.
Light penetration into a tunnel varies considerably with the site conditions as well as tunnel geometry. Short tunnels, defined as those less than 125 m (410 ft) in length, receive insufficient attention regarding their lighting requirement largely due to the misconception that natural light can penetrate through them, thus eliminating the need for artificial lighting. However, past studies and data reveal that this assumption may not necessarily hold true, with short tunnels experiencing higher crash rates compared to longer ones, partly due to visibility and driver's adaptation to lighting conditions. Field observations further show that certain regions within short tunnels have insufficient lighting, making smaller objects difficult to see. Moreover, there is a notable lack of reliable national guidelines for providing supplemental lighting within short tunnels. This underscores the need to study light penetration conditions in short tunnels. In this paper, a numerical model was developed to address this need. The model is based on the inverse square law of light propagation and the Lambert cosine law of illumination, which serve as the foundation for simulating lighting propagation in this study. Our proposed numerical model can quantitatively determine the spatial variation of light illuminance within a short tunnel. The model's outputs were independently verified and validated using field collected data. Consequently, with more studies and refinements, this model can potentially be further developed into a design and analysis tool in the future to establish lighting requirements and improve driver safety in short tunnels.
This research presents a comprehensive review of existing literature on train driver assistance systems and associated technologies suitable for onboard railway implementation. The primary objective is to systematically identify recent technological advancements, analyze their development patterns and prevalence, and gain a deeper understanding of current research trends in railway onboard systems. To achieve this, articles published since 2015 were retrieved from the Scopus database, resulting in a selection of 179 relevant studies. Each article was manually reviewed to ensure a thorough evaluation of its objectives, methodologies, and reported impacts. The selected studies were categorized into five key domains: train condition monitoring, which focuses on predictive maintenance and system health diagnostics; environment monitoring, addressing external conditions such as weather and track status; object detection, involving obstacle identification and collision avoidance; driver monitoring, which examines human factors such as attention, fatigue, and cognitive state; and brake assistance systems, aimed at improving safety and operational efficiency. This structured classification enabled a clearer comparison of technological maturity and research emphasis across different areas. Furthermore, the review explores opportunities to enhance human-machine cooperation by linking these findings with the latest developments in railway driver advisory systems (R-DAS). Based on this synthesis, four promising future research directions are identified: adaptive trajectory optimization for energy-efficient and context-aware driving, cooperative R-DAS (CR-DAS) enabling collaborative decision-making between human drivers and automation, human-in-the-loop (HITL) shared control strategies to balance authority between operator and system, and robust remote operation with reliable authority transfer mechanisms. These directions highlight the potential for more intelligent, adaptive, and cooperative onboard systems in next-generation railways.
In recent years, environment perception technology for intelligent vehicles has made significant progress, providing robust support for the intelligent level, safety, and efficiency of vehicles. However, as urban traffic environments become increasingly complex and performance requirements rise, vehicle environment perception technology urgently needs further enhancement. To better analyze the research directions of intelligent vehicle environment perception, this comprehensive review offers a thorough summary of cutting-edge technologies in sensor technology and various modules of vehicle environment perception, advancing the development of intelligent driving. First, the primary types of sensors used in intelligent vehicle environment perception and their working principles are introduced, emphasizing the importance of sensor technology. The review then focuses on the current research status in key areas such as lane and road detection, traffic sign recognition, vehicle detection, and scene understanding. These technologies enable vehicles to comprehend and navigate their surroundings, which is crucial for intelligent driving tasks. The advantages and disadvantages of various research methods and algorithm models are summarized and evaluated. Finally, the future research trends in vehicle environment perception are outlined, aiming to address the current technical challenges. Overall, this review demonstrates the rapid development in the field of intelligent vehicle environment perception, providing a comprehensive evaluation of the technologies and innovations. This will offer crucial support for intelligent vehicle environment perception to progress towards a highly efficient future.
License plate detection and recognition (LPDR) is crucial for intelligent transportation systems (ITS) to ensure traffic safety and control. LPDR systems are widely used in traffic monitoring, vehicle safety, vehicle-to-vehicle (V2V) communication, and reducing traffic accidents. Modern technologies like autonomous driving and traffic optimization require secure V2V communication. Vehicles can use the LPDR system to identify nearby vehicles to communicate safely. With the expansion of applications in daily life, LPDR is facing many challenges. From the simple static camera used at the parking lot entrance, the LPDR systems are currently used for dynamic recognition of vehicle license plates (LPs), which is very difficult due to camera movement, camera angle, and distance from the vehicles. Recognizing LPs from different countries using a single algorithm is challenging since different nations use different characters, and LPs comprise multiple lines. For instance, Arabic characters could be more challenging to recognize. This article provides a performance comparison of several real-time tested and simulated LPDR methods. An ideal LPDR system must eliminate the challenges arising from new applications. The LPDR system needs to be designed to work accurately on static/dynamic conditions to develop V2V communication. By categorizing existing well-known LPDR approaches into conventional and machine learning techniques, this review tried to clarify the importance of each type of method. This work aims to review LP detection, character segmentation, and character recognition algorithms and provide guidance on future trends in this area.
Due to the increasing density and complexity of the highway network, a deep understanding of the characteristics of lane changing (LC) behavior is crucial for road refinement design. The emergence of full time domain trajectory big data provides unprecedented opportunities for in-depth research on highway safety geometry design. This article proposes a method framework for extracting LC trajectory patterns to explore the combination trajectory patterns during the LC process. This article achieves subdivision in driving mode detection by using the adaptive pruned exact linear time (APELT) algorithm to detect change points, taking into account the short sequence features of LC. In order to achieve the classification of segmented fragments, we report a clustering technique based on similarity matching (SM), which can effectively avoid the problem of excessive distortion in similarity measurement. The results indicate that APELT technology has certain advantages in F1 score and accuracy of LC pattern recognition, which is more in line with reality. The kappa score based on similarity matching is greater than 0.8, indicating high accuracy of pattern recognition. This study provides a novel data mining method for a comprehensive understanding of lane changing behavior under full time domain big data, which will provide reference for road design in complex scenes.
The application of renewable energy contributes to the sustainable development of road infrastructure. Aiming at the inadequate mechanical-electrical performance of the cantilever piezoelectric energy harvester (PEH) which can be used for road electrical facilities, the shape and size of the PEH are optimized under low-frequency roadway conditions. Firstly, models of the PEH with different shapes are constructed using different methods. Then, the electrical and mechanical properties of the PEH are investigated based on the finite element method, and the shape of the PEH is determined. Finally, the electrical theory of the triangular PEH is derived and modified, and its electrical output effect is verified by experimental testing. The results show that reducing the free-end-width and length or increasing the fixed-end-width could improve the electrical performance of the PEH, but reducing the length would significantly increase the maximum stress and reduce the uniformity of stress distribution. Comprehensive analysis of the electrical and mechanical performance recommend the use of the triangular PEH. Meanwhile, the modified electrical theory is in good agreement with finite element and test results, and a single PEH reach an output power of 15.76 mW. The results help to improve the mechanical-electrical performance of PEH, which can provide a basis for the design of self-powered road electrical facilities.
In today’s world, urban planning requires intelligent parking management for dealing with congestion and providing convenience to users. Efficient allocation of parking slots is one of the key challenges faced by planners, especially when there is rapid growth in population. Most of the existing systems rely upon database and historical data for predicting future demands. However, the existing systems cannot account for changes in user behavior and real-world situations. This paper provides an automatic parking allocation system using ensemble learning and boosting methods. The system uses historical data as well as current data like location of users and availability of slots. Using an adaptive learning process, the system accurately predicts future demands and allocates slots accordingly. The system also eliminates the problem of wastage due to cancellation of reservation requests. With intelligent prediction and allocation capability, this parking allocation system provides efficient management of parking lots within digital urban landscapes. Performance assessment for the suggested framework is carried out through the use of standard performance measures, which illustrate the efficiency of the ensemble boosting algorithm. It can be observed from the experimental analysis that the combination of XGBoost and random forest results in a success rate of 98.8%, surpassing traditional techniques. In addition, it is ensured that there is optimal use of resources, considering efficient allocation of the parking slots and related performance factors.
This review aims to enhance the mechanical and road performance of cold recycled asphalt mixture, and advocate the application of cold recycling technology, which features lower energy consumption and reduced carbon emissions. The properties of composition materials were initially studied for cold recycled asphalt mixture, meanwhile which regeneration mechanism was subsequently revealed. Additionally, a summary of the impact of complex factors on the mechanical and road performance of cold regenerated mixtures was provided, along with a proposed method for optimizing their performance. This review summarizes the main challenges of current cold recycling technologies and discusses future research directions. From numerous studies, it is found that the composition of cold recycled asphalt mixture significantly impacts its performance. The gradation variability and dosage of reclaimed asphalt pavement (RAP) material are the main factors affecting the performance of cold recycled material. The bonding property of binder has a significant effect on the mechanical properties of the mixture. The cold regeneration mechanism of waste asphalt mixture includes the rejuvenation of aged asphalt, the bonding mechanism of binder and the enhancement impact of additives. There are many factors affecting the performance of cold reclaimed asphalt mixture. Currently, improvements in mechanical and road performance can primarily be achieved through three main avenues: controlling the quality of RAP material, modifying the binders, and incorporating high-performance additives. However, the cold recycled asphalt mixture still faces several challenges, including a low utilization rate of RAP and limited application structure layers. In addition, insufficient early strength delays the opening of traffic. In the future, it is still necessary to propose more reasonable and efficient approaches to optimize the performance of cold reclaimed asphalt mixture, diverging from traditional methods. Moreover, the intelligent monitoring, evaluation, and prediction of performance for cold recycled asphalt pavement is also highly essential.
The evolution of artificial intelligence (AI) technologies has seen an excessive utility across several industrial domains including railways. In general, railway infrastructure is composed of various components like track, overhead electrical systems, signaling system and communication system. The name of these components varies with different countries; for instance, Swedish inference for the railway infrastructure components is named as Bana, El, Signal, Tele (BEST) corresponding to track, electricals, signaling and telecom. The purpose of this paper is to provide a comprehensive review on the AI technologies utilized in the maintenance of railway infrastructure. Thereby highlighting the significance of AI in facilitating the execution of maintenance activities carried out in railway infrastructure. In this study, a total of 99 scientific papers were reviewed that were published between January 2019 and April 2024. The selected papers were reviewed based on the AI techniques used in the maintenance of railway infrastructure and were further categorized into major AI technologies within components in the railway infrastructure. The analysis based on the literature survey states that most of the operation and maintenance activities revolved around the detection of track and catenary defects. Whilst, only limited or no research was found related to AI in the fields of signaling and telecom. However, the adoption and application of AI in maintenance of railway infrastructure are still at the infant stages. A large scope persists in developing a combined application of AI and multitude of techniques such as feature fusion, point cloud data, image analysis, etc., that can be adopted for effective decision-making, enhanced optimisation, ease to handle uncertainties and tackle cybersecurity related issues.
Road traffic emissions, characterized by cross-regional mobility, require collaborative inter-regional reduction efforts for sustainable transportation development. However, certain regions reduce their own abatement efforts by free-riding on emission control measures implemented by neighboring areas, thereby undermining collaborative governance. This study develops a tripartite evolutionary game model to investigate the strategic dynamics of inter-regional emission control under heterogeneous traffic emission conditions. The model explores the evolutionary interactions and strategic adaptations of the three involved stakeholders. Numerical simulations further reveal dynamic changes in strategic choices and identify the key factors influencing the evolutionary process. The results reveal a dual-impact mechanism of regulatory intensity between two regions with heterogeneous traffic emission. Notably, regions with limited emission reduction capacity tend to engage in free-riding, capitalizing on the abatement efforts of neighboring areas. Although inter-regional emission interaction intensity does not directly influence regulatory strategies, higher levels of connectivity significantly increase the risk of free-riding behavior. The optimal evolutionarily stable strategy combination is “positive emission reduction, positive emission reduction, and moderate regulation”, indicating that moderate regulation can enhance collaborative control of inter-regional traffic emissions. The system exhibits multi-path and multi-stage evolutionary trajectories, with regulatory costs, subsidy rates, and regional emission reduction capacities emerging as key determinants.
