Traffic Infrastructure Research Articles

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.

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.

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.

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.

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.

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.

The effect of place of living on social acceptance of shared PV projects in Switzerland

In Switzerland, solar power has the greatest potential to further advance the energy transition. As conventional rooftop PV excludes a large share of citizens from personally contributing to climate change mitigation, shared PV (photovoltaics) projects offer a more inclusive alternative, e.g. also for urban residents who are less likely to have the option to install solar panels on their own roof. Thus, investigating people’s preferences depending on the degree of urbanity of their place of residence is crucial to determine socially acceptable PV project designs. Using original Swiss survey data (N = 3000) and applying conjoint analysis, different preferences both depending on respondents’ place of living as well as for different dimensions of social acceptance are found. Most notably, big city residents are generally most in favour of such projects but they exhibit the lowest willingness to actually invest. The conjoint experiment showed that, regarding the DVs of project rating and project choice, only the reimbursement by credit vouchers significantly improved project acceptance for big city residents. This also holds true for rural residents, where, in addition, tax deductions (reimbursement), traffic infrastructure, large consumer roofs (location) and electricity provider were also positively evaluated. For the DV specifying the amount of PV modules bought, no project attribute level managed to significantly increase this number, for which only investment-reducing factors were identified: Residents from big cities invest less when a PV project is located in a skiing area or when the investment is made at an information event. For rural residents, large consumer roofs as a location decrease the scale of an investment. These factors should therefore be avoided when planning a shared PV project in order to maximise investments. Fulfilling respective preferences (i.e. according to projects’ place-based target group) may prove relevant to enhance social acceptance of shared PV projects.

Analysis the Reliability of Travel Time in Urban Corridors in Baghdad City

Travel-time reliability is a crucial performance measure for transportation systems. This research aims to estimate travel time and predict delay time on three routes in Baghdad city using GPS devices: Safi Al-Den Street, Palestine Street, and the Army Channel Expressway. The study was conducted northward from 7:00 a.m. to 9:00 p.m. Reliability indices for the first and second routes with signalized intersections, including buffer index, travel time index, and 95th percentile travel time, were determined. Safi Al-Den Street's buffer time index for links 1 to 3 is approximately 44.75%, 39.87%, and 39.12%, respectively. The highest travel time index value is observed in link 3 at 8.9%. Link 1 has the longest 95% travel time at 659 seconds. Palestine Street shows a high buffer time index in links 1 and 3 at around 32.39% and 24.65%, respectively. The highest travel time index is in link 3 at 6.84%, attributed to congestion from increased educational, medical, and commercial trips. The longest 95% travel time is 430 seconds in link 7. This study offers valuable insights into the reliability of travel time in urban corridors in Baghdad city, aiding transportation planners and engineers in making informed decisions on traffic management, infrastructure development, and policy-making.

Design of Traffic Control Solutions and Infrastructure Alignment to Overcome Congestion Problems in Batam City.

Batam City is one of the cities that experiences significant traffic congestion problems, especially around the Traffic Light intersection. This research aims to identify and analyze the factors that influence the smoothness of traffic at the Batam City Traffic Light Intersection, as well as designing integrated solutions to overcome these problems. Through study stages involving data collection, analysis of key factors causing traffic jams, and modeling the impact of each factor, this research tries to provide recommendations for improving infrastructure, redesigning intersections, using traffic light technology, and providing education to motorists. It is hoped that the results of this research can serve as a guide for the government and stakeholders in improving traffic management and quality of life in Batam City.

Identifying Street Environmental Factors That Attract Public Attention from the Jogger’s Perspective: A Multiscale Spatial Exploration

Improving street jogging environments can promote urban health and sustainability, especially within urban built-up areas. Public attention to the environment during jogging has not been sufficiently studied, and the recommended route data from the fitness app offers an opportunity for multiscale spatial exploration. Initially, by analyzing the recommendation reason text, the study identified 22 environmental factors and proposed the Environmental Factor Public Attention Intensity (EFPAI) for quantification, with subsequent comparisons to expert opinions. Further, by analyzing five types of data from the recommended routes, the relationships between EFPAI and jogging frequency, route shape, spatial distribution, and seasonal variations were uncovered. The study revealed: (1) discrepancies between expert opinions and public attention, with experts more likely to overlook factors such as slope, visual quality, width, and acoustic quality; (2) the public tends to notice factors like pedestrians, green spaces, waterfront spaces, landscapes, culture, sports facilities, and accessibility on routes with high jogging frequency; (3) EFPAI for traffic infrastructure, waterfront spaces, landscapes, residential areas, and campuses is associated with route shape; (4) EFPAI of certain environmental factors is influenced by urban geographical spatial factors, and substantial differences exist between different cities. Specific recommendations were provided from three different scales—site scale, street block scale, and urban scale—to guide the resolution of issues in planning and decision-making processes for urban renewal.