Mobile phone distraction has emerged as a major social and road safety issue, contributing to rising rates of traffic accidents, injuries, and fatalities. Understanding how phone use influences driver behavior is therefore essential. This study investigates drivers’ attitudes and behaviors regarding mobile phone use while driving through a questionnaire survey of 613 participants across multiple regions of Anbar Governorate, Iraq. Results reveal that 68% of drivers admit to using their phones while driving, most frequently on urban roads. Notably, 51.4% reported never switching their phones to silent mode, despite having access to modern vehicles and higher education. Logistic regression analysis identified behavioral factors—particularly responding to calls while driving (p < 0.001) and a history of phone-related accidents (p < 0.001)—as significant predictors of phone use, whereas demographic factors showed no significant association. These findings highlight the prevalence of risky driving behaviors linked to mobile phone distraction and underscore the need for targeted interventions to enhance road safety. Keywords: Mobile phone distraction, Driving behavior, Road safety, Traffic accidents

Between pause and pulse: How travel time shapes opt-out preferences in Hong Kong’s urban street experiments

Identifying critical areas in urban road networks: A grid-based approach considering route redundancy

Using active-source‐induced distributed acoustic sensing signals for detection of urban road voids

Vulnerability analysis of urban road traffic network under cyber attacks on ride-hailing platforms

Signal priority control method for connected transit at urban road intersections considering group energy conservation

Percutaneous metal absorption from airborne particulate matter: evaluating the role of skin barrier integrity.

Spatial distribution prediction and scale effect analysis of urban daytime noise based on remote sensing images: a case study of Chengdu.

In order to improve the precision and reliability of the objective evaluation of vehicle vibration, a multi-condition drivability test is designed based on the GB/T4970-2009 standard. The three-axis vibration and acceleration data in the speed range of 30–120 km/h are collected from urban roads, national highways, and motorways. Combined with the improved empirical modal decomposition (EMD) method, a vibration signal optimization strategy with dynamic frequency band adjustment and double threshold screening is proposed. The empirical modal decomposition algorithm solves the problem of modal aliasing, establishes a speed-based frequency band adjustment mechanism and a suspension intrinsic frequency model, and constructs a dual-threshold filtering mechanism with instantaneous frequency probability density (≥85%) and energy entropy criterion, which effectively separates the driver operation from the noise interference. Compared with various improvement methods, the EMD dynamic dual-threshold method has the best correction effect, and the correction error of the integrated weighted acceleration root mean square value is reduced to 0.191%. The method breaks through the severe limitations of the traditional test conditions and provides a highly robust analysis framework for vehicle vibration comfort evaluation.

Extracting road features from high-resolution remote sensing imagery is crucial for urban planning, navigation systems. However, this task is challenged by factors such as occlusions from buildings, interruptions in road continuity, and variations in road width and appearance. These issues often lead to segmentation discontinuities and misclassifications. This paper presents the Urban Road Extraction Network (UBR-Net), an architecture that enhances the DeepLabv3+ model to address these challenges. The key contributions lie in the specific design and integration of several modules. We introduce Cross-Residual Encoding blocks designed to preserve fine-grained details and mitigate the gradient vanishing problem, thereby improving road continuity. Additionally, UBR-Net incorporates a Multiscale Context Features Extraction (MCFE) module, enhanced with an Improved Self-Attention Block (ISAB), to capture rich, hierarchical feature representations with a focus on long-range dependencies. A Channel Spatial Attention Module (CSAM) is also integrated to refine the feature extraction process by focusing on critical channels and spatial regions. Evaluations on public datasets, including DeepGlobe and Massachusetts, show that UBR-Net reduces extraction errors from occlusions, achieving a 75.18% F1 score and a 57.22% Intersection over Union (IoU), surpassing existing methods. These results highlight UBR-Net’s effectiveness for urban analysis and its potential for more precise and efficient urban planning.

The number of crashes involving non-motorized vehicles (NMVs), such as bicycles and electric bicycles, on urban roads is high, so it is crucial to explore the factors influencing NMV safety. However, to date, most studies have relied on limited models that fail to capture the spatial heterogeneity in the effects of factors (e.g., bike lane and bike lane width) on NMV crash frequency. This study investigated the spatial heterogeneity in NMV crashes on urban roads using a geographically weighted random forest model. Three categories of data including roadway, bicycle facility, and traffic operation characteristics on 585 segments in Shanghai, China were collected for the development of the model. The results indicated significant spatial variations in the effects of key factors in the three categories of data on NMV crashes on urban roads. Roadway factors were important, especially segment length and the number of accesses, which were positively correlated with NMV crashes. The importance of access in some segments of the northwestern and northeastern areas was high. As for bicycle facilities, greenbelts and guardrails had substantial effects on NMV crashes, and greenbelts were negatively correlated with NMV crashes. The importance of the absence of physical separation was high in the northwestern area. The importance of average speed was also high, as it negatively affected NMV crashes and had a greater effect on NMV crash frequency in the northwestern area. These findings could help policy makers of crash-prone segments implement targeted practical engineering solutions and safety countermeasures to improve NMV safety.

Urban redevelopment is essential to improving sustainability and livability, but traffic congestion is still a problem, not only during rush hour but also during construction, accidents, and other interruptions. Existing research frequently concentrates only on optimizing traffic flow, with little consideration given to stakeholder-driven viewpoints or environmental and anthropogenic risk concerns. Although Geographic Information Systems (GIS) have great promise for gathering and evaluating vast amounts of spatial data, little is known about how they might be used in comprehensive frameworks for traffic risk assessment. To fill these shortcomings, this study suggests a brand-new hybrid framework for evaluating traffic sustainability that combines GIS with a Convolutional Neural Network (CNN) model and a Multi-Criteria Decision-Making (MCDM) technique. By (i) using a fuzzy Delphi method to systematically weight various environmental and anthropogenic criteria, (ii) utilizing oversampling methods to address data imbalances in risk prediction, and (iii) embedding CNN-based modeling in an interactive GIS platform to produce fine-grained, stakeholder-relevant risk maps, the framework significantly improves on previous research. The usefulness of the framework is demonstrated by a case study conducted in Foshan City, Guangdong Province, where road collapse segments were divided into five risk levels. Of these, 7% were categorized as high risk and 4% as extremely high risk, with the majority of these segments being in the eastern and southeastern regions. The training and testing accuracies of the Fuzzy-CNN model were 0.989 and 0.982, respectively, indicating high predictive performance. For municipal governments and urban planners, the generated traffic risk map offers a scalable and data-driven decision-support tool. This research tackles major shortcomings of current models in balancing sustainability, risk management, and urban resilience and advances the state of the art in GIS-based traffic risk analysis by explicitly integrating biodiversity and environmental elements into traffic planning.

ABSTRACT In the context of future-oriented urban road infrastructure adaptation, it is crucial to understand the factors that influence cyclists’ route choice behavior to promote cycling effectively. However, the increase in bicycle usage can also lead to crowded cycling infrastructure and increased objective safety risks, which has not been thoroughly investigated in previous studies. This study examine how crowding, safety risks, and route attributes influence cyclists’ route choices, differentiating between e-bike and regular bike users. Using stated preference data from 784 cyclists in Nanjing and a hybrid choice model (HCM), the research integrates latent attitudinal variables and socio-demographic factors. Findings show regular cyclists are more sensitive to crowding and accident risk, while e-bike users prioritize dedicated cycling infrastructure over travel time and road characteristics. The results underscore the importance of including attitudinal variables in route choice models and provide valuable guidance to safely manage rising cycling demand and improve navigation systems.

Urbanization causes changes in the original characteristics of land use, generating significant environmental impact due to increased waterproofing rates and disorderly occupation of urban roads. Over the last few years, the greater availability of medium and high resolution satellite images has made it possible to monitor urban expansion and its impact on the dynamics of surface runoff in river basins through Geographic Information Systems (GIS). Among the urban centers affected by high urbanization, the city of Caruaru-PE, has a growth rate higher than the Brazilian average, presenting great waterproofing of its natural soil, and as a consequence, serious problems with flooding. In this scenario, remote sensing images of the urban contribution basin from the years 2000, 2015 and 2020 were classified into five classes: green area, impervious area of lots, asphalt road, cobblestone road and compacted soil. The results showed a significant reduction in the green area and, almost in the same proportion, an increase in the impermeable area of the lots, while the other classes had few changes. The maximum surface runoff flow, estimated by the rational method, was 26% between the years 2000 and 2015, and 41% between the years 2000 and 2020.

Rapid urbanization has intensified the demand for street designs that reconcile ecological quality with positive human experiences, particularly in high-density cities such as Tianjin, China. Streets function as key interfaces where ecological processes, social activities and human perception intersect. However, existing research tends to emphasize the amount of greenery while overlooking its structural characteristics, to treat perception as a psychological response decoupled from spatial context, and to make limited use of fine-grained functional data to examine how ecology and perception interact. This study develops an integrated analytical framework that combines the DeepLabV3+ model to extract the Urban Street Greenery Generalized Structure (USGGS) from Baidu Street View imagery with a vision transformer model trained on the Place Pulse 2.0 dataset to derive multidimensional perceptual metrics. Functional diversity is represented using point-of-interest (POI) data, and an enhanced Light Gradient Boosting Machine (LightGBM) model is employed to explore associations among greenery structure, perceived qualities and functional characteristics. Analyses of six urban districts in Tianjin indicate that ecological and perceived street qualities are closely related to the degree of coupling between vegetation structure and functional diversity. Streets characterized by multi-layered greenery and diverse, active functions tend to exhibit higher perceived aesthetics, safety and vitality, whereas streets with single-layer vegetation or functionally monotonous environments generally do not perform as well. Functional patterns appear to mediate relationships between greening and perception by shaping how ecological form is experienced through everyday social activities. Overall, the results suggest that closer coordination between ecological design and functional organization is important for fostering urban streets that combine environmental resilience with strong perceived appeal.

Metallic element (ME) contamination in urban road dust poses critical environmental and health challenges, particularly in Dhanbad, eastern India, representing one of the most intensively coal-mining-impacted urban regions in India, with over 112 active and abandoned mines, extensive overburden dumps, and widespread coal transportation corridors. This study systematically analyzed 44 composite road dust samples to quantify multiple MEs. Spatial interpolations, various geochemical indices, integrated Positive Matrix Factorization (PMF)- human health risk (HHR) framework based on Monte Carlo simulation was applied to comprehend the risk assessment. Excluding Al, all MEs exceeded their background value. Geochemical indices revealed moderate to very high contamination levels, particularly from Cd, As, and Cr. Spatial interpolation maps highlighted that coal mining zones, overburden dumps, and traffic-dense corridors, were the most polluted sites, prevailing in the central and NW-SE directions. Four major source factors of pollution were identified as mixed sources (F1), coal mining operations (F2), automotive and industrial emissions (F3), and lithogenic/overburden rock (F4). Multivariate statistical analyses further corroborated the findings of PMF. The integrated PMF-HHR framework, coupled with Monte Carlo simulation, revealed substantial non-carcinogenic (NCR) and carcinogenic risks (CR) for both adults and children. F2 followed by F3 were the predominant contributors to human health risks. Overall, the integrated PMF-HHR framework effectively linked source contributions to quantified health outcomes, providing a reliable tool for source-oriented risk management and mitigation in coal mining-affected urban environments.

This study aims to analyse the urban road network in the city of Misurata using Space Syntax methodology. The primary goal is to understand the structural characteristics of the city's street network and its implications for urban planning and development. The importance of this research lies in its potential to inform policies and interventions for optimizing the efficiency and equity of the urban road infrastructure. The Space Syntax approach was employed to evaluate measures of integration, accessibility, and potential movement within the Misurata street network. The findings reveal significant variations in the network's topological properties across different neighbourhoods, indicating uneven distribution of urban connectivity and accessibility . Based on the analysis, the study provides recommendations for urban planners and policymakers. These include strategies to enhance connectivity in underserved areas, improve accessibility to key destinations, and foster a more equitable distribution of mobility options for the city's residents. The application of Space Syntax techniques has demonstrated its value in diagnosing and guiding the complex urban road systems

HighlightsWhat are the main findings?Effective Dataset & Augmentation Strategies: Farthest Point Sampling preserves features better than random sampling, rigid transformations enhance diversity, and noise injection improves authenticity. Point filling significantly outperforms zero-padding (86.49% train/98.23% max test acc vs. 66.86%/79.89%).Optimized Model Performance: Using optimal hyperparameters (lr = 0.00075, batch = 6, Adam, PointNet++ MSG), the model achieved 86.26% avg train acc (98.54% max) and 97.41% test acc. Most categories (e.g., biker, excavator) performed excellently, while a few (e.g., building, traffic lights) had low recall due to sample issues; minor misclassifications from small dataset/imbalance were mitigated by hyperparameter tuning.What are the implications of the main findings?This study presents a viable high-precision classification technique for autonomous driving and map creation, providing a reliable reference for related research and applications in complex road environments.The findings provide offers a reusable framework for road scene point cloud dataset construction, data augmentation, and PointNet++ modification, supporting similar 3D data processing tasks.Point cloud data, with its rich information and high-precision geometric details, holds significant value for urban road infrastructure surveying and management. To overcome the limitations of manual classification, this study employs deep learning techniques for automated point cloud feature extraction and classification, achieving high-precision object recognition in road scenes. By integrating the Princeton ModelNet40, ShapeNet, and Sydney Urban Objects datasets, we extracted 3D spatial coordinates from the Sydney Urban Objects Dataset and organized labeled point cloud files to build a comprehensive dataset reflecting real-world road scenarios. To address noise and occlusion-induced data gaps, three augmentation strategies were implemented: (1) Farthest Point Sampling (FPS): Preserves critical features while mitigating overfitting. (2) Random Z-axis rotation, translation, and scaling: Enhances model generalization. (3) Gaussian noise injection: Improves training sample realism. The PointNet++ framework was enhanced by integrating a point-filling method into the preprocessing module. Model training and prediction were conducted using its Multi-Scale Grouping (MSG) and Single-Scale Grouping (SSG) schemes. The model achieved an average training accuracy of 86.26% (peak single-instance accuracy: 98.54%; best category accuracy: 93.15%) and a test set accuracy of 97.41% (category accuracy: 84.50%). This study demonstrates successful road scene point cloud classification, providing valuable insights for point cloud data processing and related research.

In Intelligent Heterogeneous Vehicular Ad Hoc Networks (IH-VANETs), long urban roads with a high density of vehicles and a maximum number of road signals increase unpredictable delays in terms of long travel times and heavy traffic congestion. These unpredictable delays are exacerbated by the rapid increase in vehicle density and irregular traffic flow on roads with high traffic signals. To address this gap, an optimized fitness-based enhanced ant colony optimization (OF-EACO) for IH-VANETs is proposed. OF-EACO aims to find optimal, uncongested short roads with low vehicle density and fewer traffic signals, thereby providing shorter travel times for vehicles without traffic congestion and unpredictable delays. To achieve this goal, the novel road fitness function of the proposed OF-EACO assigns a high road fitness score to roads according to short length, low vehicle density, and low signal count to support quick travel of vehicles between two ends without delay and traffic congestion. OF-EACO's roulette wheel takes the road fitness scores of available roads as input and outputs the optimal road. The optimal road is rich in all aspects and is intended to reduce travel time through short and un crowded roads. A network simulator is used to simulate the proposed OF-EACO, existing vehicular multi-hop routing algorithm with intelligent transportation system (VMR-ITS), and improved distance-based ant colony optimization routing (IDBACOR).Simulation results of the proposed OF-EACO indicated that, due to the use of optimal roads, it was able to achieve significant improvements in terms of vehicle travel cost, road establishment time, convergence speed, road traffic congestion overhead, routing overhead, Computational overhead, Computational Complexity, Actual Wall Time Analysis, and Energy Consumption compared to VMR-ITS and IDBACOR.

Road traffic crashes are the 12th leading cause of death, and is the leading cause of death among the age group of 5-29 globally. Worldwide, nearly 500 children die daily, and many children suffer serious injuries. Nationally, 200 children lost their lives and 805 suffered from serious injuries as a result of road crashes in fiscal year 2024/25 (Nepal Police, 2025) in Nepal. To contribute towards improving child road safety, iRAP introduced a tool for assessing road conditions around school zones to ensure better facilities for road safety furniture, called the Star Rating for Schools (SR4S). The study is based on a school along an urban arterial road in Lalitpur district. All parameters, as outlined in the SR4S coding guide and checklist, including road environment, road features, road type, intersection, flow, speed, curve, sidewalks, and crossings, were collected based on field observations. The initial rating was obtained after coding the data into the SR4S web application. Various interventions were simulated using the SR4S System Demonstrator to analyze improvements to the existing road infrastructure and enhance safety for students. One of the major improvements was observed when the operating speed was reduced to 30 km/h, which improved the star rating to 4.3. The findings offer valuable insights for implementing targeted safety measures in the Nepalese school environment.