Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
111
result(s) for
"Traffic surveys Data processing."
Sort by:
Urban Traffic Monitoring and Analysis Using Unmanned Aerial Vehicles (UAVs): A Systematic Literature Review
Unmanned aerial vehicles (UAVs) are gaining considerable interest in transportation engineering in order to monitor and analyze traffic. This systematic review surveys the scientific contributions in the application of UAVs for civil engineering, especially those related to traffic monitoring. Following the PRISMA framework, 34 papers were identified in five scientific databases. First, this paper introduces previous works in this field. In addition, the selected papers were analyzed, and some conclusions were drawn to complement the findings. It can be stated that this is still a field in its infancy and that progress in advanced image processing techniques and technologies used in the construction of UAVs will lead to an explosion in the number of applications, which will result in increased benefits for society, reducing unpleasant situations, such as congestion and collisions in major urban centers of the world.
Journal Article
Hierarchical model for taxi crashes considering the intrinsic factors of taxi drivers and companies in South Korea
Many studies have been conducted to investigate the diverse human-related factors that contribute to traffic crashes. Human factors have a greater impact on crashes caused by taxi drivers with long driving distances and hours. However, due to issues related to the protection of individual data and the complexity of collecting and processing data, there are limitations in clearly identifying risk factors related to driver characteristics. In this study, we combined in-depth survey data that included characteristics of taxi drivers and the companies they belong to and taxi crash data (2017–2019) in South Korea. However, the combined data showed a high correlation or causality between variables, leading to potential problems, i.e., multicollinearity, hierarchical structure of data, and inefficient analysis. To address this issue, we applied Principal Component Analysis (PCA) to reduce the dimensionality of variables and mitigate the problem. Furthermore, we constructed a hierarchical model considering the hierarchical structure of data in corporate taxis, where drivers are affiliated with specific companies. The analysis revealed that managing fatigue at the company level, managing drivers’ diseases, and other intrinsic factors had a significant influence on Fatal-Injury (FI) crashes. These results indicate that taxi crashes are influenced significantly by both company management factors and driver-related factors. Therefore, policymakers can provide customized preventive measures that consider both aspects.
Journal Article
Comprehensive Approach to the Evaluation of Off-Line License Plate Recognition Data
The aim of the article is to present a comprehensive procedure for processing and evaluating directional data from vehicle license plates, focusing on the specific challenges of areas that are sparsely or not at all equipped with permanently located standard license plate recognition systems. This remains a current issue, especially in smaller towns; it leads to the implementation of short-term directional traffic surveys, often using inexpensive measurement devices, in order to obtain directional traffic data. In this research, a procedure for evaluating license plate recognition data is proposed with a primary focus on its simple adaptability and automation for any subsequent use. The data sources considered are primarily the above-mentioned traffic surveys; however, the proposed evaluation procedure is theoretically transferable to any off-line data obtained from license plate recognition systems. Identifying potential inaccuracies in the data is also an integral part of the evaluation process. The design of the proposed procedure follows the Checkland soft systems methodology and the functionality of the resulting procedure was validated through a case study of a directional survey in Prague. The proposed procedure contributes to greater accuracy of the conclusions drawn from evaluated traffic engineering parameters under non-ideal, but common conditions of smaller cities, not only in the Czech Republic.
Journal Article
Multi-view traffic sign detection, recognition, and 3D localisation
Several applications require information about street furniture. Part of the task is to survey all traffic signs. This has to be done for millions of km of road, and the exercise needs to be repeated every so often. We used a van with eight roof-mounted cameras to drive through the streets and took images every meter. The paper proposes a pipeline for the efficient detection and recognition of traffic signs from such images. The task is challenging, as illumination conditions change regularly, occlusions are frequent, sign positions and orientations vary substantially, and the actual signs are far less similar among equal types than one might expect. We combine 2D and 3D techniques to improve results beyond the state-of-the-art, which is still very much preoccupied with single view analysis. For the initial detection in single frames, we use a set of colour- and shape-based criteria. They yield a set of candidate sign patterns. The selection of such candidates allows for a significant speed up over a sliding window approach while keeping similar performance. A speedup is also achieved through a proposed efficient bounded evaluation of AdaBoost detectors. The 2D detections in multiple views are subsequently combined to generate 3D hypotheses. A Minimum Description Length formulation yields the set of 3D traffic signs that best explains the 2D detections. The paper comes with a publicly available database, with more than 13,000 traffic signs annotations.
Journal Article
An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos
Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams.
Journal Article
An overview of traffic sign detection and classification methods
Over the last few years, different traffic sign recognition systems were proposed. The present paper introduces an overview of some recent and efficient methods in the traffic sign detection and classification. Indeed, the main goal of detection methods is localizing regions of interest containing traffic sign, and we divide detection methods into three main categories: color-based (classified according to the color space), shape-based, and learning-based methods (including deep learning). In addition, we also divide classification methods into two categories: learning methods based on hand-crafted features (HOG, LBP, SIFT, SURF, BRISK) and deep learning methods. For easy reference, the different detection and classification methods are summarized in tables along with the different datasets. Furthermore, future research directions and recommendations are given in order to boost TSR’s performance.
Journal Article
A Survey of Data Augmentation Techniques for Traffic Visual Elements
Autonomous driving is a cornerstone of intelligent transportation systems, where visual elements such as traffic signs, lights, and pedestrians are critical for safety and decision-making. Yet, existing datasets often lack diversity, underrepresent rare scenarios, and suffer from class imbalance, which limits the robustness of object detection models. While earlier reviews have examined general image enhancement, a systematic analysis of dataset augmentation for traffic visual elements remains lacking. This paper presents a comprehensive investigation of enhancement techniques tailored for transportation datasets. It pursues three objectives: establishing a classification framework for autonomous driving scenarios, assessing performance gains from augmentation methods on tasks such as detection and classification, and providing practical insights to guide dataset improvement in both research and industry. Four principal approaches are analyzed, including image transformation, GAN-based generation, diffusion models, and composite methods, with discussion of their strengths, limitations, and emerging strategies. Nearly 40 traffic-related datasets and 10 evaluation metrics are reviewed to support benchmarking. Results show that augmentation improves robustness under challenging conditions, with hybrid methods often yielding the best outcomes. Nonetheless, key challenges remain, including computational costs, unstable GAN training, and limited rare scene data. Future work should prioritize lightweight models, richer semantic context, specialized datasets, and scalable, efficient strategies.
Journal Article
A survey of reinforcement and deep reinforcement learning for coordination in intelligent traffic light control
Intelligent traffic signal control is required for a transportation system to function properly. In contrast to existing traffic signals, where rules are typically developed manually, an intelligent traffic signal control system should dynamically adapt to real-time traffic. The use of reinforcement learning for intelligent traffic signal control is a growing trend, and recent studies have shown promising results. Reinforcement learning (RL) enables a single agent to learn and perform optimal actions independently, whereas multi-agent reinforcement learning (MARL) enables traffic light controllers to learn, exchange and optimize their actions. However, none of the current studies has tested actual traffic data yet. This paper presents the primary techniques and methods (RL, DL, DRL, MARL, MADRL). The analysis of each technique, the learning of its strengths and limitations, in order to evaluate at which levels, they satisfy the requirements of urban traffic. The paper also lines some of the simulators, which perform adaptive traffic. Finally, we discuss the advantages, strengths, and weaknesses of the latest transformer models and graph neural network models.
Journal Article
Digital Reconstruction Method for Low-Illumination Road Traffic Accident Scenes Using UAV and Auxiliary Equipment
In low-illumination environments, traditional traffic accident survey methods struggle to obtain high-quality data. This paper proposes a traffic accident reconstruction method utilizing an unmanned aerial vehicle (UAV) and auxiliary equipment. Firstly, a methodological framework for investigating traffic accidents under low-illumination conditions is developed. Accidents are classified based on the presence of obstructions, and corresponding investigation strategies are formulated. As for the unobstructed scene, a UAV-mounted LiDAR scans the accident site to generate a comprehensive point cloud model. In the partially obstructed scene, a ground-based mobile laser scanner complements the areas that are obscured or inaccessible to the UAV-mounted LiDAR. Subsequently, the collected point cloud data are processed with a multiscale voxel iteration method for down-sampling to determine optimal parameters. Then, the improved normal distributions transform (NDT) algorithm and different filtering algorithms are adopted to register the ground and air point clouds, and the optimal combination of algorithms is selected, thus, to reconstruct a high-precision 3D point cloud model of the accident scene. Finally, two nighttime traffic accident scenarios are conducted. DJI Zenmuse L1 UAV LiDAR system and EinScan Pro 2X mobile scanner are selected for survey reconstruction. In both experiments, the proposed method achieved RMSE values of 0.0427 m and 0.0451 m, outperforming traditional aerial photogrammetry-based modeling with RMSE values of 0.0466 m and 0.0581 m. The results demonstrate that this method can efficiently and accurately investigate low-illumination traffic accident scenes without being affected by obstructions, providing valuable technical support for refined traffic management and accident analysis. Moreover, the challenges and future research directions are discussed.
Journal Article