Traffic Infrastructure Research Articles

A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation

This research introduces a cutting-edge approach to traffic infrastructure digitization, integrating UAV oblique photography with LiDAR point clouds for high-precision, lightweight 3D road modeling. The proposed method addresses the challenge of accurately capturing the current state of infrastructure while minimizing redundancy and optimizing computational efficiency. A key innovation is the development of the TJYRoad-Net model, which achieves over 85% mIoU segmentation accuracy by including a traffic feature computing (TFC) module composed of three critical components: the Regional Coordinate Encoder (RCE), the Context-Aware Aggregation Unit (CAU), and the Hierarchical Expansion Block. Comparative analysis segments the point clouds into road and non-road categories, achieving centimeter-level registration accuracy with RANSAC and ICP. Two lightweight surface reconstruction techniques are implemented: (1) algorithmic reconstruction, which delivers a 6.3 mm elevation error at 95% confidence in complex intersections, and (2) template matching, which replaces road markings, poles, and vegetation using bounding boxes. These methods ensure accurate results with minimal memory overhead. The optimized 3D models have been successfully applied in driving simulation and traffic flow analysis, providing a practical and scalable solution for real-world infrastructure modeling and analysis. These applications demonstrate the versatility and efficiency of the proposed methods in modern traffic system simulations.

Delay management for heterogeneous traffic in vehicular sensor networks using packet fragmentation of low priority data

AbstractVehicular sensor networks (VSNs) are expected to revolutionize the transportation systems through automated decision‐making. These networks function on the communication between vehicles, traffic infrastructure, and people. One of the major challenges of VSNs is to deal with heterogeneous traffic due to the diverse nature of information and data generated by different types of sensors and devices within the vehicular environment. To ensure an effective operation of the network, it is crucial to offer a differentiated quality of service to each traffic class. Media access control (MAC) protocols offer an opportunity to provide differentiated channel access to the traffic of different priorities. This work is focused on offering comparison of recent MAC schemes in terms of their performance for heterogeneous traffic generated by VSNs. Two protocols FROG‐MAC and UrgMAC have been selected: FROG‐MAC is developed using a simple concept of packet fragmentation, where the packets of lower priority are fragmented to offer an early transmission opportunity to urgent traffic; on the other hand, urgMAC is designed using advanced cross‐layer techniques of two‐tiered service differentiation mechanism, adaptive data rate adjustment mechanism, urgency‐based contention window size adaptation, traffic type adaptive duty cycle, and multimedia message passing. It has been found that the FROG‐MAC outperforms urgMAC in terms of delay, energy consumption and packet delivery ratio.

Road Traffic Infrastructure Construction and Air Pollution Based on the Perspective of Spatial Spillover

Road traffic infrastructure construction is widely regarded as a solution to urban congestion and air pollution. Given the frequent use of non-highway roads for inter-city travel, it is plausible that such infrastructure projects could also improve air quality in neighboring regions. However, the spatial spillover effects of these projects on air pollution remain underexplored. This paper investigates the spatial impacts of road traffic infrastructure construction on urban air pollution. Using a spatial panel regression model, we analyze data from 273 cities in a period spanning 2008 to 2021. Our findings show that road infrastructure construction significantly reduces local air pollution, with results robust to concerns about endogeneity. Furthermore, we identify notable spatial spillover effects, primarily concentrated in adjacent cities. These findings suggest that the accessibility between central cities and their neighboring areas plays a critical role in shaping traffic patterns, air quality, and sustainable urban development. As a result, coordinated regional planning for road infrastructure could serve as an effective strategy to mitigate urban air pollution and promote sustainable growth.

Trust calibration through perceptual and predictive information of the external context in autonomous vehicle

Maintaining an appropriate level of trust is critical for driving safety in autonomous vehicles. While enhancing the driver’s situation awareness (SA) of system information in autonomous driving is known to significantly promote trust calibration, it remains unclear whether enhancing the driver’s SA of the external context during driving contributes to this calibration. This study addresses this gap by improving SA of the external context during Level 3 (L3) driving automation across various driving environments. Driving contexts were manipulated using distinct road conditions containing low, medium, or high contextual risks. To enhance driver’s SA of the driving context, we redesigned the in-vehicle central control panel to display real-time perceptual and predictive information about the external driving context. We hypothesized that SA of driving contexts would facilitate trust calibration rather than merely enhancing trust, allowing trust to adjust to appropriate levels under different driving conditions. Experiment 1 examined the impact of perceptual information about the road, traffic infrastructure, and surrounding vehicles on drivers’ trust. We found that driver’s trust decreased with increased contextual risk only when the reconfigured panel was used, while the number of accidents was not affected. Experiment 2 investigated the effect of predictive information about the external context on drivers’ trust by marking safe and dangerous zones around driver’s vehicle with green and red areas, respectively. We revealed that the predictive information calibrated the trust according to road conditions and increased overall trust levels, while the number of accidents was not affected. Together, these findings suggest that enhancing perception and prediction of external contexts helps drivers align their trust with contextual risk levels in L3 driving automation without compromising driving safety.

Adaptive scheduling for Internet of Vehicles using deconfounded graph transfer learning

With the rapid development of Internet of Things technology, the Internet of Vehicles has emerged as a significant application. The Internet of Vehicles connects vehicles with traffic infrastructure and other vehicles, enabling real-time information sharing and vehicle dispatching. However, in the Internet of Vehicles environment, traffic scheduling systems face challenges such as sparse raw data and environmental confounding bias, which limit the effectiveness of traditional graph-based reinforcement learning methods. In this paper, we propose a Cross-City Federated Continual Learning Framework for Spatiotemporal Graph Transfer Learning called CCFTL, which removes confounding effects on reinforcement learning and enhances few-shot transfer learning in data-scarce cities. Our approach aims to significantly improve the adaptability and scalability of cross-city vehicle networking technologies in ever-changing environments. This study acts as an innovative research of the deconfounding strategy with spatiotemporal graph meta-knowledge learning, which enables optimization of cross-city meta-knowledge transfer through a federated continual learning approach. This approach effectively reduces learning bias caused by regional differences, which enhances the model’s generalization and adaptability to complex, heterogeneous urban scenarios. Experimental results show that our framework significantly outperforms traditional methods, especially in improving the efficiency and accuracy of traffic dispatch systems in Internet of Vehicles environments with scarce data and the presence of confounding factors.

Towards Predictive Pollution Control Through Traffic Flux Forecasting With Deep Learning: A Case Study in the City of Valencia

ABSTRACTTraffic congestion represents a significant urban challenge, with notable implications for public health and environmental well‐being. Consequently, urban decision‐makers prioritize the mitigation of congestion. This study delves into the efficacy of harnessing extensive data on urban traffic dynamics, coupled with comprehensive knowledge of road networks, to enable Artificial Intelligence (AI) in forecasting traffic flux well in advance. Such forecasts hold promise for informing emission reduction measures, particularly those aligned with Low Emission Zone policies. The investigation centers on Valencia, leveraging its robust traffic sensor infrastructure, one of the most densely deployed worldwide, encompassing approximately 3500 sensors strategically positioned across the city. Employing historical data spanning 2016 and 2017, we undertake the task of training and characterizing a Long Short‐Term Memory (LSTM) Neural Network for the prediction of temporal traffic patterns. Our findings demonstrate the LSTM's efficacy in real‐time forecasting of traffic flow evolution, facilitated by its ability to discern salient patterns within the dataset.

Large language model as parking planning agent in the context of mixed period of autonomous vehicles and Human-Driven vehicles

Autonomous vehicles (AVs) are anticipated to revolutionize future transportation, necessitating updates to traffic infrastructure, particularly parking facilities, due to the unique characteristics of AVs compared to Human-Driven Vehicles (HDVs). During the transition period in which AVs and HDVs coexist, adaptable infrastructure is essential to accommodate both vehicle types. Traditional research, typically reliant on complex mathematical models and simulations, faces challenges in adapting to diverse urban settings, requiring substantial time and resources. To address these challenges, a government-level framework was developed, enabling urban planners to quickly and accurately evaluate and optimize existing parking facilities for future AV and HDV coexistence scenarios. The framework integrates a Large Language Model (LLM) to enhance flexibility and efficiency in parking planning throughout the transitional period. Structured guidance is incorporated to enhance decision-making precision and reduce LLM hallucination risks. The flexibility, robustness, and accuracy of the framework were validated through step-by-step and end-to-end testing using real-world datasets. Specifically, the framework achieved 91.1 % comprehensiveness and 70.2 % consistency in Indicator Selection Module testing, a 68.9 % success rate in the Single Indicator Calculation Module, and a 66.7 % success rate in end-to-end testing, demonstrating its practical value in supporting cities during AV integration. Finally, the success rates of different LLM agent modules were further explored, along with a comparison of multiple LLMs and an analysis of key issues related to LLM trustworthiness in urban planning applications. The research highlights the potential of LLMs in advancing urban planning processes and optimizing existing infrastructure, contributing to smarter and more adaptable urban environments.

Comprehensive Analysis of the Transport Sector in Narayanganj City: Existing Scenario and Way Forward

This study examines transportation issues in Narayanganj City Corporation, one of the densely populated cities in Bangladesh. It examines the Chashara intersection which is a major intersection within the city, suffers from ever worsening traffic and poor infrastructure. Using qualitative research methods, information was obtained through field surveys, observation as well as four focus group discussions with residents aged 20-50 years mainly students, urban dwellers experts. Some of the problems that emerged from the study include poor traffic control, limited road construction and maintenance. The findings also highlight significant challenges, including poor traffic management, insufficient road networks, and inefficient regulation of transport services, notably the prevalence of auto-rickshaws contributing to the congestion. The study identifies the Chashara intersection as a critical area requiring urgent intervention. Proposed solutions emphasize the introduction of alternative routes to alleviate bottlenecks, stricter regulation of auto-rickshaw operations, and the implementation of congestion pricing mechanisms to manage traffic flow. These recommendations aim to provide a strategic framework for improving urban mobility and ensuring sustainable development in the transportation sector of Narayanganj. The need for immediate, coordinated efforts from local authorities and urban planners is underscored, as addressing these issues is essential for enhancing the city’s livability and accommodating future growth.

Virtual Reality Study on Pedestrians’ Perceived Trust in Interactions with Automated Vehicles

This paper investigates the impact of different automated vehicle (AV) communication concepts on pedestrians' perceived trust when interacting with the AV in an urban intersection setting. For this, two virtual reality (VR) studies were conducted. The first examined the influence of vehicle automation on how pedestrians interpret driving behavior, while the second analyzed the effects of explicit communication concepts on pedestrians' perceived trust. The results show that AV driving behavior evokes similar associations as manually driven vehicles, with overall perceived trust scores not differing considerably (with driver: 4.7, without driver: 4.6). However, qualitative interviews revealed that pedestrians ultimately interpret AV behavior differently, yielding insights into the applicability of driving behavior of manually driven vehicles as a model for AV driving behavior. The second study showed an increase in pedestrians' perceived trust when vehicle-based explicit communication was present (mean: 4.9). In contrast, infrastructure-based communication showed no difference compared to implicit communication (mean: 4.6). The investigation of the influence of the traffic infrastructure (presence of traffic lights) has shown different results for implicit and explicit communication of AVs. For implicit communication, pedestrians' perceived trust increased with traffic lights (mean: 4.8) compared to scenarios without them (mean: 4.4). However, for explicit communication, the perceived trust decreased with traffic lights (vehicle-based: 4.7, infrastructure-based: 4.6) compared to scenarios without traffic lights (mean: 5.1). One reason for this is possible cognitive overload caused by the perception of multiple visual aids that are not always in the same field of view.

A Comprehensive Security Risk Assesment Framework SRAF_IoV for Internet of Vehicles.

The advancement in digital world have led to the development of smart vehicles that connect to the Internet of Vehicles (IoV) Infrastructure. These smart vehicle also connects and communicate with other vehicles, and their connected components. Once connected, they can share information such as speed, direction, and distance between vehicles.IoV enhances traffic infrastructure by reducing congestion and incidents, ultimately improving road safety within highways and metropolitan cities. However, when smart vehicles use IoV to share data through vehicle-to-vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to everything (V2X) they also introduce security risks and vulnerabilities to the IoV Infrastructure. Malicious actors can exploit these vulnerabilities to alter data within the connected units of IoV infrastructure, such as electronic control units, onboard units, control networks, and sensors. Securing data within the IoV ecosystem is crucial to prevent malicious activity that could compromise confidentiality, integrity, and availability. In this study, a security risk assessment framework for IoV (SRAF-IoV) has been developed to identify vulnerabilities within the IoV network. SRAF-IoV that assesses security risks in connected vehicles. It evaluates vulnerabilities and assigns a risk level based on different communication channels (V2V, V2I, and V2X). The proposed framework was tested against various cyber-attacks to determine risk levels associated with different attack types (low, medium, high, and critical). The performance of SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV can push the smart vehicle industry to make critical security decisions during the development of smart vehicles.

A systematic risk assessment approach for urban roadside infrastructure assets

Revealing potential risks and the influence of these risks on urban roadside vulnerable/risky zones has been a great deal for the last decades owing to increasing vehicular mobility. This study aimed to develop a risk assessment methodology for urban roadside infrastructure assets. In this context, seven risk parameters that are likely to affect hazardous aboveground assets installed on urban roadsides were determined, and weights of each parameter (order of importance) were presented based on expert surveys and literature information using the Analytic Hierarchy Process (AHP) method. Mathematical models were set up for each risk parameter using real field data, The American Association of State Highway and Transportation Officials (AASHTO), literature, related standards, and expert surveys by linear, nonlinear, and binary logistic regression analyses. Risk groups were created, and precautions were offered. The efficiency of the models was verified based on the 28 accident records and field observations in 172 assets. When considering the assets that undergo traffic accidents, 71% of the total assets are ranked in the critical and high-risk group. This ratio is consistent with the risk group definitions assigned in this study; thus, the model proposed is accurate and can be applied reliably. The verification process of the total risk model was sufficiently successful to be used in practice and can be generalized by considering the road and traffic characteristics, infrastructure facilities, and future requirements of the regions or countries that reflect similar historical and cultural concerns. Practitioners, governmental institutes, and researchers can apply this methodology to provide their own data with high transparency and reliability.

Impact of urban form on carbon emissions of residents in counties: evidence from Yangtze River Delta, China.

Urban form is a key factor affecting carbon emissions. Accurately estimating the impact of urban form on the carbon emissions of residents (CER) is an important prerequisite for China to adopt effective low-carbon spatial planning strategies and respond to climate change. However, counties mainly account for China's energy consumption, and the relationship between their urban form and the CER remains unclear, limiting their low-carbon development. Therefore, in this study, the PLS-SEM model and data from 90 counties in the Yangtze River Delta are used to determine the extent and ways that urban form affects the CER. The model considers the impact of both geometric-aspect urban form factors (urban scale, compactness, spatial structure, and urban shape) and built environment-aspect urban form factors (public service facilities, urban greening, road traffic, public transportation, and urban energy infrastructure) on the CER. The results indicate that urban form factors related to the built environment (public service facilities, urban greening, road systems, and municipal infrastructure) have a direct impact on the CER. Geometric-aspect urban form factors (size, compactness, spatial structure, and shape) not only directly affect the CER but also indirectly affect the CER by altering built environment-aspect urban form factors. This study identifies the complex relationship between urban form and the CER, facilitating the coordinated integration of multiple elements and providing a basis for the formulation of low-carbon spatial planning strategies for counties.

Evaluation of the effectiveness of road safety measures and their impact on the pattern and distribution of extracranial and intracranial hemorrhages in fatal head injury cases in Uttar Pradesh, India

Introduction: In Uttar Pradesh, India, there are many fatal head injuries as a result of road traffic accidents (RTAs). Studying the pattern and distribution of intracranial hemorrhages, a frequent complication of severe head trauma might provide vital information on the efficacy of traffic safety regulations. To improve road safety tactics and lower fatal head injuries in Uttar Pradesh, this study intends to assess the effect of road safety measures on the frequency and distribution of intracranial hemorrhages in fatal head injury patients. Aim: This study’s objective is to assess the influence of current road safety initiatives on intracranial bleeding distribution and patterns in fatal head injury cases in Uttar Pradesh. Methodology: The 604 RTA cases with head injuries that were brought to the mortuary of the S.R.N. Hospital in Prayagraj for a medical-legal postmortem assessment over the course of a year, from April 2021 to March 2022, make up the entirety of this prospective study. Following receiving institutional ethical approval and informed consent, structured interviews with attendees utilizing a questionnaire were done to gather the data. Result: The results show that certain traffic safety measures have reduced the frequency of fatal head injuries in RTAs. A decreased incidence of intracranial hemorrhages was linked to improved traffic infrastructure, including well-planned junctions, pedestrian crossings, and distinct bicycle lanes. Similar results were shown in serious head injuries when traffic restrictions including speed limits, seat belt use, and helmet legislation were strictly enforced. Conclusion: There are still issues with fatal brain injuries from car accidents, despite some encouraging results. Safety legislation violations, a lack of public knowledge, and inadequate enforcement all contribute to the issue. Disadvantaged groups like walkers and users on two-wheelers continue to be especially susceptible. The results of this investigation offer important new understandings of the efficiency of traffic safety measures and their influence on the distribution and pattern of cerebral hemorrhages in Uttar Pradesh, India. The results point to the necessity of improved public education efforts and traffic safety laws. More research and focused interventions are required to address specific risk factors among various road user groups. This will result in a safer driving environment and a decline in catastrophic brain injuries.

Fusion of computer vision and piezoelectric tactility approach to measuring moving vehicle loads

Vehicle overloading seriously threatens the safety and long-term operation of global traffic infrastructure. The emergence of computer vision-based non-contact weigh-in-motion (NC-WIM) technique has made it possible to expand the coverage of vehicle load monitoring, but misjudgment of vehicle weight due to abnormal tire pressure is still an insurmountable drawback of this approach. In this study, we propose a novel approach to measure moving vehicle loads by fusing computer vision and piezoelectric tactility. Meanwhile, a visual-tactile sensing system based on the proposed method is built on a real road. Field tests show that the proposed method can effectively avoid the misjudgment caused by abnormal tire pressure, and the errors of vehicle weight estimation are within the range of ± 10 % in both underinflated and overinflated cases. The proposed solution not only ensures the accuracy of overload warnings, but also promises to provide support for the design, operation, and maintenance of transport infrastructure.

Analyzing India's National Civil Aviation Policy and Global Trends in Contemporary Civil Aviation Policies

The expansion of the Indian economy is expected to extend to remote regions, and while market-driven processes are ideal for regional air connectivity, there is recognition of the valuable role of public policy in this endeavor. The UDAN Scheme follows a market-oriented approach where operators assess demand, propose routes, and navigate variables like traffic rights, slot allocation, regulatory restrictions, and infrastructure limitations. Global airline industry deregulation has introduced healthy competition, enhancing efficiency and service quality, leading to increased passenger traffic. Studies have highlighted the constructive impact of improved air connectivity on the economic development of regions. Efficient airports are vital economic catalysts, but research suggests that some regional airports operate inefficiently, missing opportunities for profitability. Government subsidies and policies for enhancing regional and remote air connectivity are common practices worldwide, with schemes like EAS in the United State and PSO in Europe bridging the accessibility gap. Research is crucial to understand policy effects on air connectivity and identify factors that improve outcomes. Route dispersal guidelines, such as those in India, can create financial redistribution between route categories, affecting airlines' profitability and serving state capitals over remote regions. Policymakers must find effective ways to enhance air transport to underserved areas where market dynamics might not naturally establish viable transportation links. In summary, this study emphasizes the importance of government-supported policies like UDAN and the role of market-oriented approaches in promoting regional air connectivity. It underlines the need for research to assess policy impacts and identifies the challenges and opportunities in enhancing air connectivity to remote and underserved regions.

Simplified approach to estimating the loading response of traffic infrastructures

Various numerical methods are commonly used to model the loading response of traffic infrastructures. However, the broader implementation of these methods into practice is typically limited by the high computing cost or technical complexity of the models. Computationally lighter methods are especially needed when a substantial number of calculations must be performed. Hence, this paper introduces a new simplified approach to estimating the loading response of different traffic infrastructures, e.g. roads and railways. This approach is based on the novel approximate solution for the vertical and shear stress fields caused by a uniformly distributed circular surface load. The presented theory has been validated using versatile field measurement data from different types of road structures under varied loading conditions. In the verification calculations, both the vertical stresses within the structure and the deflection of the road surface were examined. The findings indicate that the model closely aligns with the behavior observed in actual structures. The approach presented in this paper enables a quick and easy determination of the loading response of multi-layer traffic infrastructures, providing a new practical calculation tool for researchers and practitioners.

Research Progress of Optical Satellite Remote Sensing Monitoring Asphalt Pavement Aging

The aging condition of asphalt pavement is an invaluable basis for traffic infrastructure evaluation. Due to the amount of time and high cost of monitoring and identifying asphalt pavement aging, many current studies focus on satellite remote sensing methods. In this paper, some methods and technologies for monitoring asphalt pavement degradation by optical satellite remote sensing are introduced as a literature review. Many researchers have developed spectrum libraries based on the actual aging of asphalt pavements, and it is possible to construct pavement health indices based on spectrum changes. Some indexes can extract different aging degrees of asphalt pavement from optical satellite images. Of course, current research can only preliminarily reflect the aging phenomenon of asphalt pavement and cannot accurately describe the distress characteristics of asphalt pavement. Future research needs to further strengthen mechanism research, develop higher resolution images, improve image processing technology, and adopt multi-means fusion analysis methods.

Challenges and Opportunities in integrating UTMS with Existing Airspace

The aviation industry has both potential and substantial obstacles as it integrates unmanned aerial vehicles (UAVs) into regulated airspace. In order to ensure safe and effective operations in shared airspace, this study examines the difficulties associated with integrating unmanned traffic management (UTM) systems with the current air traffic management infrastructure. By means of an in-depth examination of technological and operational factors, the research delves into the principal obstacles that stakeholders encounter when attempting to regulate the growing number of unmanned aerial vehicles. To promote seamless integration, the study also identifies areas for innovation, including standardized communication protocols, traffic flow management, and cooperative decision-making frameworks. This essay offers important insights into the future of autonomous traffic management and its consequences for the larger aviation ecosystem by fusing current academic findings with industry insights.

Research on the health monitoring and maintenance strategy of highway structuresg

Under the current background of The Times, China's economic and social development presents a good situation, the construction of transportation infrastructure has been better developed, and the highway transportation industry has made outstanding achievements. At the present stage, the highway transportation industry has become an important pillar industry of China's national economy, and is an important guarantee to promote economic growth and facilitate people's travel. Highway transportation infrastructure is an important foundation for China's national economic and social development, especially in the process of "Belt and Road" construction, highway transportation infrastructure has played a vital role. Under the influence of the increasing number of expressways and heavy-haul transport vehicles, there are also some problems in the highway traffic infrastructure. Therefore, this paper studies and analyzes the health monitoring and maintenance strategy of highway structure, aiming to provide some reference for further improving the health monitoring and maintenance work of highway structure in China.

On CO2 efficiency and trade-offs between sustainability, safety, economy and resilience in infrastructure developments

The escalating crisis of climate change underscores the pressing need to reduce global CO2e emissions substantially. At the same time, a major increase is expected in global demand for infrastructure systems. In meeting this challenge, we here provide and illustrate a novel modeling framework that affords decision support in pursuit of sustainable developments of infrastructure systems. Based on this framework, for the case of roadway traffic infrastructure systems, it is shown that different strategies for the development of roadway traffic infrastructure systems, yielding approximately the same economic benefits to society, can differ in their embedded carbon footprint by up to a factor of three. This knowledge has the potential to contribute significantly to sustainable developments in general and mitigation of climate change in particular. Our models and results may readily assist decision-makers in the construction industry, at national and supra-national scales, in identifying optimal strategies for infrastructure developments. Furthermore, our work may straightforwardly support regulatory bodies and code writers in defining requirements for material and structural performances for the next generation of standards and codes.