Search Results (36)

With the advancement in intelligent transportation systems, single-sensor perception solutions face inherent limitations. To address the constraints of monocular vision detection, this study presents a vehicle road detection system that integrates millimeter-wave radar and visual information. By generating mask maps from millimeter-wave radar point clouds, radar data transition from a global assistance role to localized guidance, identifying vehicle target positions within RGB images. These mask maps, along with RGB images, are processed by a Dual Cross-Attention Module (DCAM), where the fused features are fed into an enhanced YOLOv5 network, improving target localization accuracy. The proposed dual-input DCAM enables dynamic feature fusion, allowing the model to adjust its reliance on visual and radar data according to environmental conditions. To optimize the network architecture, ShuffleNetv2 replaces the YOLOv5 Backbone, while the Ghost Module is incorporated into the Neck, creating a lightweight design. Pruning techniques are applied to reduce model complexity, making it suitable for embedded applications and real-time detection scenarios. The experimental results demonstrate that this fusion scheme effectively improves vehicle detection accuracy and robustness compared to YOLOv5, with accuracy increasing from 59.4% to 67.2%. The number of parameters is reduced from 7.05 M to 2.52 M, providing a precise and reliable solution for intelligent transportation and roadside perception. Full article

In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals. Full article

Wild horse crossing events are a major concern for highway safety in rural and suburban areas in many states of the United States. This paper provides a practical and real-time approach to detecting wild horses crossing highways using 3D light detection and ranging (LiDAR) technology. The developed LiDAR data processing procedure includes background filtering, object clustering, object tracking, and object classification. Considering that the background information collected by LiDAR may change over time, an automatic background filtering method that updates the background in real-time has been developed to subtract the background effectively over time. After a standard object clustering and a fast object tracking method, eight features were extracted from the clustering group, including a feature developed to specifically identify wild horses, and a vertical point distribution was used to describe the objects. The classification results of the four classifiers were compared, and the experiments showed that the support vector machine (SVM) had more reliable results. The field test results showed that the developed method could accurately detect a wild horse within the detection range of LiDAR. The wild horse crossing information can warn drivers about the risks of wild horse–vehicle collisions in real-time. Full article

Childhood asthma is a major health issue in Australia, and traffic emissions play a causative role. Two urban planning policies that impact children’s exposure to traffic emissions are considered in terms of the potential health risks to children in a Melbourne suburb with high truck volumes and hospital attendances for childhood asthma. Firstly, the health impact assessment component of the state planning approval of a major road project, and secondly, local government placement of childcare centres and schools in relation to freight routes. Three sources of air quality monitoring data were examined: (i) a Victorian EPA reference site; (ii) a site with planning approval for development into a childcare centre; and (iii) five sites within the boundary of the West Gate Tunnel Project, an AUD 10 billion road and tunnel project. The Australian Urban Research Infrastructure Network data was utilised to assess distances of childcare centres and schools from major truck routes. A range of cconcentration–response functions for childhood asthma (0–18 years) from international systematic meta-analyses and a smaller Australian cross-sectional study were applied to comparative elevations in fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations between the EPA reference monitor (used for project risk assessment) and local roadside data. It was found that comparative elevations in NO₂ concentrations were associated with the following risk increases: developing asthma 13%, active asthma 12%, and lifetime asthma 9%. Overall, 41% of childcare centres (n = 51) and 36% of schools (n = 22) were ≤150 m to a high-density truck route. Truck emissions likely make a substantial contribution to childhood asthma outcomes in the project area. This study exemplifies how current practices may not be commensurate with guiding policy objectives of harm minimisation and equitable protection. Full article

The automotive sector faces escalating security risks due to advances in wireless communication technology. Expanding on our previous research using a sensor pairing technique and machine learning models to evaluate IoT sensor data reliability, this study broadens its scope to address security concerns in Connected and Autonomous Vehicles (CAVs). The objectives of this research include identifying and mitigating specific security vulnerabilities related to CAVs, thereby establishing a comprehensive understanding of the risks these vehicles face. Additionally, our study introduces two innovative pairing approaches. The first approach focuses on pairing Electronic Control Units (ECUs) within individual vehicles, while the second extends to pairing entire vehicles, termed as vehicle pairing. Rigorous preprocessing of the dataset was carried out to ensure its readiness for subsequent model training. Leveraging Support Vector Machine (SVM) and TinyML methods for data validation and attack detection, we have been able to achieve an impressive accuracy rate of 97.2%. The proposed security approach notably contributes to the security of CAVs against potential cyber threats. The experimental setup demonstrates the practical application and effectiveness of TinyML in embedded systems within CAVs. Importantly, our proposed solution ensures that these security enhancements do not impose additional memory or network loads on the ECUs. This is accomplished by delegating the intensive cross-validation to the central module or Roadside Units (RSUs). This novel approach not only contributes to mitigating various security loopholes, but paves the way for scalable, efficient solutions for resource-constrained automotive systems. Full article

Globally, road traffic collisions cause over a million deaths annually, with pedestrians accounting for 23%. In developing countries, most pedestrian deaths occur on urban arterial roads, particularly at midblock crossings. This study analyzes the factors influencing driver yielding behavior at midblock crosswalks on urban arterial roads in Thailand. This study analyzed the factors influencing driver yielding behavior at the midblock crosswalk before and after the upgrade from a zebra crossing (C1) to a smart pedestrian crossing (C2), which is a smart traffic signal detecting and controlling pedestrians and vehicles entering the crosswalk. Video-based observations were used to assess driver yielding behavior, with multinomial logistic regression applied to develop driver yielding behavior models. The results revealed that the chances of a driver yielding at C2 were higher than at C1, and the yielding rate increased by 74%. The models indicate that the number and width of traffic lanes, width and length of crosswalks, vulnerable group, number of pedestrians, pedestrian crossing time, number of vehicles, vehicle speed, headway, post-encroachment time between a vehicle and pedestrian, and roadside parking are the significant factors influencing yielding behavior. These findings propose measures to set proper crosswalk improvements (e.g., curb extensions), speed reduction measures, enforcement (e.g., parking restrictions), public awareness campaigns, and education initiatives. Full article

As the size and complexity of cities around the world increase, various types of urban problems are emerging. These problems are caused by multiple factors that have complex relationships with each other. Addressing a single cause blindly may result in additional problems, so it is crucial to understand how urban problems arise and how their causes interact. The study utilizes the Grey Decision-Making Trial and Evaluation Laboratory (Grey-DEMATEL), in conjunction with the Grey Interpretative Structural Model (Grey-ISM), to construct a hierarchical structure that examines the relationships between the causes of urban problems, thereby revealing the root causes and developmental mechanisms of urban problems. The Grey Matrix Cross-Reference Multiplication Applied to Classification (Grey-MICMAC) method was employed to clarify the influence and position of each cause. The Poor Parking Convenience (PPC) in Wuhan, China, is taken as a case study. The findings reveal the following: (1) the proposed method effectively identifies the key causes and processes of urban problems; (2) the insufficient management of roadside parking areas and impractical allocation of temporary parking spaces are the two main causes of PPC in Wuhan City. This method would be helpful to urban managers in discovering the causes of urban problems and formulating corresponding policies, to ultimately contribute towards healthy urban and sustainable development. Full article

Although the hawthorn is not a forest-forming species, and it has no high economic significance, it is a very valuable component of forests, mid-field woodlots or roadside avenues. The literature, however, lacks information on the growth rate, growth phases, or growth–climate–habitat relationship for trees of this genus. This work aimed to establish the rate of growth of Craraegus monogyna and C. xmedia Bechst growing in various parts of Poland, in various habitats; analyze the growth–climate relationship; and distinguish dendrochronological regions for these species. Samples were taken using a Pressler borer from nine populations growing in different parts of Poland, from a total of 192 trees (359 samples). The tree-ring width was measured down to 0.01 mm. The average tree-ring width in the studied hawthorn populations ranged from 1.42 to 3.25 mm/year. Using well-established cross-dating methods, nine local chronologies were compiled with tree ages between 45 and 72 years. Dendroclimatic analyses (pointer year analysis, correlation and response function analysis) were performed for a 33-year period from 1988 to 2020, for which all local chronologies displayed EPS > 0.85. The tree-ring width in the hawthorn populations depended mostly on temperature and rainfall through the May–August period. High rainfall and the lack of heat waves through these months cause an increase in cambial activity and the formation of wide tree rings. Conversely, rainfall shortages through this period, in conjunction with high air temperatures, caused growth depressions. Cluster analysis enabled the identification of two dendrochronological regions among the hawthorn in Poland: a western and eastern region, and a single site (CI), whose separation was most likely caused by contrasting habitat and genetic conditions. The obtained results highlight the need for further study of these species in Poland and other countries. Full article

Diabetes is a chronic disease that is escalating day by day and requires 24/7 continuous management. It may cause many complications, precisely when a patient moves, which may risk their and other drivers’ and pedestrians’ lives. Recent research shows diabetic drivers are the main cause of major road accidents. Several wireless non-invasive health monitoring sensors, such as wearable continuous glucose monitoring (CGM) sensors, in combination with machine learning approaches at cloud servers, can be beneficial for monitoring drivers’ diabetic conditions on travel to reduce the accident rate. Furthermore, the emergency condition of the driver needs to be shared for the safety of life. With the emergence of the vehicular ad-hoc network (VANET), vehicles can exchange useful information with nearby vehicles and roadside units that can be further communicated with health monitoring sources via GPS and Internet connectivity. This work proposes a novel approach to the health care of drivers’ diabetes monitoring using wearable sensors, machine learning, and VANET technology. Several machine learning (ML) algorithms assessed the proposed prediction model using the cross-validation method. Performance metrics precision, recall, accuracy, F1-score, sensitivity, specificity, MCC, and AROC are used to validate our method. The result shows random forest (RF) outperforms and achieves the highest accuracy compared to other algorithms and previous approaches ranging from 90.3% to 99.5%. Full article

Vehicle trajectory data play an important role in autonomous driving and intelligent traffic control. With the widespread deployment of roadside sensors, such as cameras and millimeter-wave radar, it is possible to obtain full-sample vehicle trajectories for a whole area. This paper proposes a general framework for reconstructing continuous vehicle trajectories using roadside visual sensing data. The framework includes three modules: single-region vehicle trajectory extraction, multi-camera cross-region vehicle trajectory splicing, and missing trajectory completion. Firstly, the vehicle trajectory is extracted from each video by YOLOv5 and DeepSORT multi-target tracking algorithms. The vehicle trajectories in different videos are then spliced by the vehicle re-identification algorithm fused with lane features. Finally, the bidirectional long-short-time memory model (LSTM) based on graph attention is applied to complete the missing trajectory to obtain the continuous vehicle trajectory. Measured data from Donghai Bridge in Shanghai are applied to verify the feasibility and effectiveness of the framework. The results indicate that the vehicle re-identification algorithm with the lane features outperforms the vehicle re-identification algorithm that only considers the visual feature by 1.5% in mAP (mean average precision). Additionally, the bidirectional LSTM based on graph attention performs better than the model that does not consider the interaction between vehicles. The experiment demonstrates that our framework can effectively reconstruct the continuous vehicle trajectories on the expressway. Full article

Infrastructure 3D Object Detection is a pivotal component of Vehicle-Infrastructure Cooperated Autonomous Driving (VICAD). As turning objects account for a high proportion of traffic at intersections, anchor-free representation in the bird’s-eye view (BEV) is more suitable for roadside 3D detection. In this work, we propose CetrRoad, a simple yet effective center-aware detector with transformer-based detection head for roadside 3D object detection with single LiDAR (Light Detection and Ranging). CetrRoad firstly utilizes a voxel-based roadside LiDAR feature encoder module that voxelizes and projects the raw point cloud into BEV with dense feature representation, following a one-stage center proposal module that initializes center candidates of objects based on the top N points in the BEV target heatmap with unnormalized 2D Gaussian. Then, taking attending center proposals as query embedding, a detection head with multi-head self-attention and multi-scale multi-head deformable cross attention can refine and predict 3D bounding boxes for different classes moving/parked at the intersection. Extensive experiments and analyses demonstrate that our method achieves state-of-the-art performance on the DAIR-V2X-I benchmark with an acceptable training time cost, especially for Car and Cyclist. CetrRoad also reaches comparable results with the multi-modal fusion method for Pedestrian. An ablation study demonstrates that center-aware query as input can provide denser supervision than a purified feature map in the attention-based detection head. Moreover, we were able to intuitively observe that in complex traffic environment, our proposed model could produce more accurate 3D detection results than other compared methods with fewer false positives, which is helpful for other downstream VICAD tasks. Full article

University neighborhoods in Taiwan have high-volume traffic, which may increase motorcyclists’ risk of injury. However, few studies have analyzed the environmental factors affecting motorcycle crash injury severity in university neighborhoods. In this multicenter cross-sectional study, we explored the factors that increase the severity of such injuries, especially among young adults. We retrospectively connected hospital data to the Police Traffic Accident Dataset. Areas within 500 m of a university were considered university neighborhoods. We analyzed 4751 patients, including 513 with severe injury (injury severity score ≥ 8). Multivariate analysis revealed that female sex, age ≥ 45 years, drunk driving, early morning driving, flashing signals, and single-motorcycle crashes were risk factors for severe injury. Among patients aged 18–24 years, female sex, late-night and afternoon driving, and flashing signals were risk factors. Adverse weather did not increase the risk. Time to hospital was a protective factor, reflecting the effectiveness of urban emergency medical services. Lifestyle habits among young adults, such as drunk driving incidents and afternoon and late-night driving, were also explored. We discovered that understanding chaotic traffic in the early morning, flashing signals at the intersections, and roadside obstacles is key for mitigating injury severity from motorcycle crashes in university neighborhoods. Full article

The United Nations (UN) stated that all new roads and 75% of travel time on roads must be 3+ star standard by 2030. The number of stars is determined by the International Road Assessment Program (iRAP) star rating module. It is based on 64 attributes for each road. In this paper, a framework for highly accurate and fully automatic determination of two attributes is proposed: roadside severity-object and roadside severity-distance. The framework integrates mobile Lidar point clouds with deep learning-based object detection on road cross-section images. The You Only Look Once (YOLO) network was used for object detection. Lidar data were collected by vehicle-mounted mobile Lidar for all Croatian highways. Point clouds were collected in .las format and cropped to 10 m-long segments align vehicle path. To determine both attributes, it was necessary to detect the road with high accuracy, then roadside severity-distance was determined with respect to the edge of the detected road. Each segment is finally classified into one of 13 roadside severity object classes and one of four roadside severity-distance classes. The overall accuracy of the roadside severity-object classification is 85.1%, while for the distance attribute it is 85.6%. The best average precision is achieved for safety barrier concrete class (0.98), while the worst AP is achieved for rockface class (0.72). Full article

Parthenium weed (Parthenium hysterophorus L.) is among the most noxious invasive alien plant species, which can pose a threat to agro- and native-ecosystems. Despite potential parthenium infestation risks at the south-western regions of the Ganges–Brahmaputra floodplains of Bangladesh, no studies exist that document parthenium infestation. Using field surveys and a maximum entropy (Maxent) modelling approach, the present study tries to address the problem in the concerned region comprised of five Districts: Jashore, Jhenaidah, Chuadanga, Meherpur, and Khustia. The results revealed high infestation in the Jashore, Jhenaidah, and Chuadanga Districts, mainly along roadsides, in grasslands, and in fallow and cropped fields. The greatest abundance of the weed (ca. 30 plants m⁻²) occurred at the Indian border area, suggesting cross-border spreading, possibly through the linking road systems. Furthermore, we found that under both low and high emissions scenarios (Representative Concentration Pathways 2.6 and 8.5), parthenium weed suitability areas were likely to expand, suggesting an increased threat to the agro-ecosystems of Bangladesh. The present study is the first attempt to survey and model potential parthenium weed distribution affecting one of the major hubs of agricultural production in Bangladesh. The findings of this study can help land managers to make judicious decisions towards the future management of these agro-ecosystems. Full article

Road traffic elements comprise an important part of roads and represent the main content involved in the construction of a basic traffic geographic information database, which is particularly important for the development of basic traffic geographic information. However, the following problems still exist for the extraction of traffic elements: insufficient data, complex scenarios, small targets, and incomplete element information. Therefore, a set of road traffic multielement remote sensing image datasets obtained by unmanned aerial vehicles (UAVs) is produced, and an improved YOLOv4 network algorithm combined with an attention mechanism is proposed to automatically recognize and detect multiple elements of road traffic in UAV imagery. First, the scale range of different objects in the datasets is counted, and then the size of the candidate box is obtained by the k-means clustering method. Second, mosaic data augmentation technology is used to increase the number of trained road traffic multielement datasets. Then, by integrating the efficient channel attention (ECA) mechanism into the two effective feature layers extracted from the YOLOv4 backbone network and the upsampling results, the network focuses on the feature information and then trains the datasets. At the same time, the complete intersection over union (CIoU) loss function is used to consider the geometric relationship between the object and the test object, to solve the overlapping problem of the juxtaposed dense test element anchor boxes, and to reduce the rate of missed detection. Finally, the mean average precision (mAP) is calculated to evaluate the experimental effect. The experimental results show that the mAP value of the proposed method is 90.45%, which is 15.80% better than the average accuracy of the original YOLOv4 network. The average detection accuracy of zebra crossings, bus stations, and roadside parking spaces is improved by 12.52%, 22.82%, and 12.09%, respectively. The comparison experiments and ablation experiments proved that the proposed method can realize the automatic recognition and detection of multiple elements of road traffic, and provide a new solution for constructing a basic traffic geographic information database. Full article