Road Safety Management Research Articles

A Two-Phase Approach for Efficient Traffic Sign Detection and Recognition

Objectives: The objective of this study is to enhance the accuracy of traffic sign detection and recognition in modern intelligent transport systems, addressing real-time challenges under varying conditions. Methods: A two-phase approach is adopted. The first phase employs the You Only Look Once version 8 (YOLOv8) architecture to efficiently detect traffic signs under real-time conditions, considering variables like adverse weather and obstructions. Subsequently, the second phase employs a sequential convolutional network for precise recognition, utilizing the output from the first phase. This integrated method enhances traffic sign detection and recognition, contributing to road safety and efficient traffic management in complex transportation scenarios. Findings: The YOLOv8 architecture, utilized in Phase 1, demonstrated exceptional performance with a mean Average Precision (mAP) of 0.986 during validation. In Phase 2, the Convolutional Neural Network (CNN)-based recognition model achieved an impressive test accuracy of 98.7% on 463 test images, with a low-test loss of 0.1186, indicating consistent accuracy. The robustness of both models is confirmed by successful testing with three unseen images. YOLOv8 accurately detected and classified these images, while the CNN model correctly recognized them. These findings underscore the effectiveness of the two-phase approach in enhancing traffic sign detection and recognition, with significant implications for improving road safety and traffic management in real-world scenarios. Novelty: The novelty of this approach lies in its seamless integration of YOLOv8 for efficient traffic sign detection and a sequential convolutional network for accurate recognition, offering a significant advancement in addressing real-time challenges and contributing to enhancing road safety and traffic management in an increasingly complex transportation landscape. Keywords: Traffic sign detection, Traffic sign recognition, Convolutional Neural Networks, YOLOv8, Object detection

A multiscale enhanced pavement crack segmentation network coupling spectral and spatial information of UAV hyperspectral imagery

Road pavement cracks are a critical factor affecting the health conditions of road pavements. Accurate crack detection contributes to providing data support for road maintenance measures. Compared to conventional crack detection algorithms, deep learning based crack segmentation methods have practical significance for road maintenance and traffic safety management due to their high precision and automation. However, existing deep learning methods often suffer from segmentation accuracy loss due to the varying crack sizes and the presence of stains on the pavement with spatial characteristics similar to cracks, leading to the misclassification of cracks. Therefore, this study proposed a multiscale enhanced road pavement crack segmentation network (MS-CrackSeg) by coupling spectral and spatial information to detect pavement cracks from unmanned aerial vehicle (UAV) hyperspectral imagery. MS-CrackSeg can simultaneously learn the spatial and rich spectral features of cracks in the hyperspectral imagery, improving the discrimination of those targets with similar spatial features to cracks compared to previous approaches. Moreover, the Multiscale Self-Attention-like Feature Extraction Module (MSSA) is introduced to extract and fuse multiscale crack features to enhance the crack detection. Experiments on a dataset consisting of 1031 hyperspectral images demonstrated the superior crack segmentation of the proposed method compared to the comparative methods. In particular, the proposed method achieved the highest F1-score of 0.74 and mean Intersection over Union of 0.79, indicating an exceptional performance. The developed approach offers improved data support for road maintenance measures and has validated the advantages of UAV hyperspectral imagery in road crack segmentation. The annotated hyperspectral dataset and the code for MS-CrackSeg network are available at https://github.com/williamchen-x/MS-CrackSeg.

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm

Road traffic collisions are among the world’s critical issues, causing many casualties, deaths, and economic losses, with a disproportionate burden falling on developing countries. Existing research has been conducted to analyze this situation using different approaches and techniques at different stretches and intersections. In this paper, we propose a two-layer ensemble machine learning (ML) technique to assess and predict road traffic collisions using data from a driving simulator. The first (base) layer integrates supervised learning techniques, namely k- Nearest Neighbors (k-NN), AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method, using logistic regression as a meta-classifier. In addition, the synthetic minority oversampling technique (SMOTE) was performed to handle the data imbalance before training the model. To simplify the model, the particle swarm optimization (PSO) algorithm was used to select the most important features in our dataset. The proposed two-layer ensemble model had the best outcomes with an accuracy of 88%, an F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost. The proposed two-layer ensemble model can be used in the future for theoretical as well as practical applications, such as road safety management for improving existing conditions of the road network and formulating traffic safety policies based on evidence.

Assessment of road safety performance based on Entropy-RSR model

This paper develops a hybrid decision-making model, Entropy-RSR, for benchmarking road safety for Southeast Asian countries at the regional level. This model generates ranking and grouping of the countries, which provides policymakers and practitioners with a valuable tool for policymaking and decision-making in road safety management.

SCORING METHOD OF EVALUATING ROAD SAFETY AS PART OF MANAGEMENT SYSTEM

Known methods of assessing the level of traffic safety on highway sections are considered. Factors that can significantly affect forecasting accuracy have been identified. It is proposed to improve the existing method by introducing additional parameters, which were obtained as a result of the formalization of accident statistics at the places of traffic accidents concentration. The purpose of the work is to provide traffic safety specialists with an effective express method of evaluating the level of traffic safety with points directly on the road. The proposed scoring method of evaluating road safety also arouses interest due to the recent implementation in Ukraine of a road safety audit at the stage of operational maintenance, which is specifically designed to conduct an independent inspection of dangerous sections of highways and prescribe low-cost and effective measures to reduce accidents. This will help to make decisions in the road safety management system that will relate to effective measures.

Decision analysis with IDOCRIW-QUALIFLEX approach in the 2TLq-ROF environment: An application of accident prediction models in Pakistan

In Pakistan, the assessment of road safety measures within road safety management systems is commonly seen as the most deficient part. Accident prediction models are essential for road authorities, road designers, and road safety specialists. These models facilitate the examination of safety concerns, the identification of safety improvements, and the projection of the potential impact of these modifications in terms of collision reduction. In the context described above, the goal of this paper is to utilize the 2-tuple linguistic q-rung orthopair fuzzy set (2TLq-ROFS), a new and useful decision tool with a strong ability to address uncertain or imprecise information in practical decision-making processes. In addition, for dealing with the multi-attribute group decision-making problems in road safety management, this paper proposes a new 2TLq-ROF integrated determination of objective criteria weights (IDOCRIW)-the qualitative flexible multiple criteria (QUALIFLEX) decision analysis method with a weighted power average (WPA) operator based on the 2TLq-ROF numbers. The IDOCRIW method is used to calculate the weight of attributes and the QUALIFLEX method is used to rank the options. To show the viability and superiority of the proposed approach, we also perform a case study on the evaluation of accident prediction models in road safety management. Finally, the results of the experiments and comparisons with existing methods are used to explain the benefits and superiority of the suggested approach. The findings of this study show that the proposed approach is more practical and compatible with other existing approaches.

Evaluating the Efficacy of Real-Time Connected Vehicle Basic Safety Messages in Mitigating Aberrant Driving Behaviour and Risk of Vehicle Crashes: Preliminary Insights from Highway Scenarios

Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including car-following, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management.

Development of a Network-Level Road Safety Assessment Procedure Based on Human Factors Principles

Road safety is a central issue in the management and development of a road network. Road agencies must try to identify the most dangerous sections of their network and act on them to improve safety. The most used procedure for this purpose is about considering the indicators based on crashes. However, a mature road safety management system must be able to assess the safety of a road section before accidents occur. The European community is moving in this direction with the update of Directive 2008/96/EC (Directive 1936/2019). This paper proposes a new methodology for carrying out a network-wide road safety assessment on rural single-carriageways and two-lane two-way roads. This procedure accounts for the influence of road characteristics on drivers’ perceptions. The methodology has been developed based on the human factors concepts from PIARC, and it includes a series of checklists that guide an inspector in carrying out a visual inspection of single-carriageway roads. The results from the checklist are then processed into an algorithm, and the level of risk in the analyzed section is provided. The objectives of the procedure are (a) to account for the perceptive aspects that are one of the major causes of road accidents, (b) to provide a proactive procedure in line with the requirements of the European Directive, and (c) to provide a useful instrument that can be easily implemented by road agencies and integrated with other analysis procedures. The procedure has been applied and tested on a case study of six different stretches of two-lane, two-way rural highways in Italy, Germany, and Slovenia (about 65 km). The results show a high degree of concordance with a risk classification based on the accident rate, mainly considering high-risk sections. Therefore, the procedure demonstrated its potential to be a useful instrument to be included in network safety assessments. Road agencies should consider the use of this procedure in their network safety analysis and ranking.

Exploring the Impact of Rainfall on Vehicle Trajectory Patterns and Sideslip Risk: An Empirical Investigation

Understanding the sideslip risks of various trajectory patterns, as well as the impact of rainfall on them, is critical for improving road safety. However, the lack of precise classification indicators hampers systematic analysis of the variations in vehicle trajectory patterns. To address this, this study proposes a parameterized classification method for trajectories on curved segments, employing the radius and offset of the trajectory as the primary classification features and dividing the trajectories into nine patterns. These patterns represent variations from smaller to larger radii and inside to outside lane offsets, reflecting different driving behaviors and vehicle stability during vehicle cornering. Concurrently, the friction coefficient utilization rate is used to effectively compare vehicles’ sideslip risk under different weather conditions. Based on this, we construct a framework using computer vision technology for automatically identifying trajectory patterns and measuring sideslip risk. We conducted an empirical study on a highway-curved segment with high sideslip risk in China and collected two datasets under clear and rainy conditions for analysis. The classification results show that the proposed method can effectively classify trajectories according to nine trajectory patterns. Comparative analysis reveals that vehicle trajectories in both the inside and outside lanes are notably more affected by rainfall compared to the middle lane. Meanwhile, trucks demonstrate a higher susceptibility to rainfall than cars. In addition, the analysis of the sideslip risk for different trajectory patterns discovers several high-risk patterns. This study provides an effective approach for monitoring and analyzing the sideslip risk on curved segments, thereby contributing to the enhancement of road design and traffic safety management.

Road Safety Challenges: Assessing Deficiencies and Preventive Culture in Peru

Purpose: The purpose of this research is profound and vital: to seek to understand the relationship between road safety management and preventive culture in the face of traffic accidents, focused on the city of Chachapoyas, Peru. Theoretical Framework: Sheds light on an alarming panorama worldwide. Deficiencies in road safety management, evidenced in areas such as infrastructure, vehicle condition, victim care and in institutional management itself, are an urgent call for attention. The current preventive culture leaves much to be desired, with measures that, although present, do not achieve the expected effectiveness. Drivers show negligent attitudes, vehicles have technical deficiencies and factors such as driving under the influence of substances aggravate the problem. Design / Methodology / Approach: applied research was chosen. The structure of this study was based on a non-experimental cross-sectional correlational design. The sample was carefully selected, composed of 61 workers of the transit regulatory agency. Results: The results were not encouraging. It was found that road safety management is rated at a worrying 71% at an inadequate level. At the same time, accident prevention culture reached 69% in the same category. Statistical indicators, such as Kendall's Tau-b and Spearman's Rho, support the relevance of these results. Research, practical and societal implications: Research is even more exciting when considering the impact of technology on road safety and driver psychology. How do safety systems adapt and evolve? What is the driver's reaction to these innovations? From a more human perspective, understanding driver motivations and attitudes is vital for future interventions. The practical and societal implications of this study are enormous. It highlights the imperative need to review our institutions and regulations. It is not enough to establish policies; their effective implementation must be ensured. At the same time, it shows the importance of educational campaigns, infrastructure improvement and a rigorous review of vehicle regulations. Originality / Value: The originality and value of this study lies in its focus on Chachapoyas, Peru, an analysis that, despite its geographic specificity, may offer valuable insights for other regions. The combination of technological and psychological aspects of road safety presents a rich and promising avenue of research.

Comprehensive Safety Index for Road Safety Management System

A safety-index-based road safety management system (RSMS) is a tool to help identify locations where safety intervention is needed. To date, various safety indices have been developed and utilized, but it is rare to consider the plan–do–check–act structure in an RSMS when studying the decision-making methodology. In this study, 36 indicators and a system of evaluation indicators were selected based on the major classifications of performance, effect, and improvement. Performance was categorized by safety system components and effect was reflected in the safety status, such as the number of injuries. The indicators were validated, and a classification methodology for safety groups was proposed through cluster analysis. It was found that there was no correlation between the indicators and the population, budget, or road area by administrative district. It was also found that no particular indicators had a significant impact on the overall result in the major category or the overall index. It was determined that the developed indicators were suitable for administrative district-specific safety monitoring. It is expected that these indicators will be continuously utilized and enhanced in the national evaluation of road traffic safety.

ВЛИЯНИЕ НА РИСК ГИБЕЛИ В ДТП В РОССИЙСКИХ ГОРОДАХ CТРУКТУРНОЙ СПЕЦИФИКИ ДОРОЖНО-ТРАНСПОРТНОЙ АВАРИЙНОСТИ

The article presents the results of studying the structural specifics of the processes of formation of road traffic accidents rate in the cities - regional centers of the Central Federal District of the Russian Federation. The specifics of the structure of road accidents are considered from the standpoint of quantifying the risk of death in an accident of an average resident of the city. It was found that the risks of death in an accident during 2023 for residents of 17 cities – administrative centers of the Central Federal District differ by almost 8 times. This is a very serious level of variability in the risk of death in an accident. Another important result of the research is the establishment of a fundamentally different structure of the processes of accident formation in different cities. This fact must be taken into account when choosing road safety management strategies in various Russian cities.

Study of traffic safety on the street and road network of Ternopil

The road safety issues in the city of Ternopil are studied in this article. The found actual deficiencies were structured according to the main indicators. Based on the analysis of the movement of traffic flows in the morning period, the most problematic sections of the SRN (street and road network) were identified. In general, the speed of traffic flows along the SRN in the evening peak period was found significantly lower than in the morning peak period. To analyze road traffic safety in the city of Ternopil, a cartographic method was applied. Based the obtained results, the road accidents in Ternopil mostly occur along main streets without clearly defined places of concentration. In this regard, a conditional indicator of the danger of transport corridors of Ternopil was derived, which allows analyzing the predominant location (clustering) of road accidents with victims in each district of the city. The current policy and practices of Ternopil in the field of road safety management need improvement. However, the volume of performed measures does not fully cover the need in traffic safety insurance and information provision of traffic participants. In general, it can be stated that proactive actions in the field of ensuring traffic safety are just starting in Ternopil (“starting city”). Thus, it differs favorably from the vast majority of other regional centers, in which the problem of traffic safety has not been even discussed so far. In addition, the plan for sustainable urban mobility is under consideration in the Ternopil City Territorial Community.

Нормативно-правові аспекти будівництва автомобільних доріг

Statement of the problem. Road building is an important task of the State in modern society, and therefore it requires special attention from the state authorities. Warfare has caused catastrophic destruction on a large Ukraine territory, which has also affected the road surface in many regions. Therefore, Ukraine suffers from these problems to a large extent, which requires adequate management actions by the state authorities and proper regulatory support for efficient road building, which can bring the industry to a fundamentally new level of development. Analysis of recent research and publications. The problems of functioning of the road building sector are studied in the works of such scientists as A. Bezuglyi, T. Zakha- rova, V. Ivanchenko, N. Ishchenko, V. Krasniuk, O. Nepomniashchyi, I. Novakovska, Y. Sereda, B. Stasiuk, M. Stetsiuk, M. Yanchuk and others. They analyse the relevance of implementing the European experience of road safety management in the public administration system, study the peculiarities of road safety management, carry out a comprehensive assessment of the efficiency indicators of road building investments, substantiate the ecological and economic principles of land use of road transport and road economy, and study the peculiarities of public administration of road construction. Identification of previously unresolved parts of the general problem. The subject of this research is to study the peculiarities of regulatory and legal support for the road building. Summary of the basic research material. The analysis of the regulatory and legal support for road building shows that its legal regulation is sufficient. However, there are problems relating to the efficient and legal use of subvention funds, which is directly related to the corruption component. In this regard, it is necessary: to reduce corruption risks, to bring Ukrainian legislation closer to European requirements; to amend the Law "On Public Procurement" to require the use of an electronic procurement system; to regulate the prohibition of procurement of services for routine medium-term maintenance of roads by individual sections; to improve the advisory and personnel issues. Conclusions. In order to solve the problems that exist in the regulatory and legal sphere of road building, it is necessary to harmonise Ukrainian legislation with the European legislative framework, to amend the relevant regulatory documents to increase the level of transparency of tendering in the process of road building, and to ensure an appropriate level of expert advice during the implementation of works related to road building.

A hybrid deep learning method for distracted driving risk prediction based on spatio-temporal driving behavior data

ABSTRACT Timely exact distracted driving risk prediction is beneficial to perceive real-time traffic risk, which is an essential but challenging task in modern traffic safety management. The use and improvement of measures for road safety management will be better facilitated by grasping and analyzing the spatio-temporal patterns of driving behavior and forming predictions. In this paper, a Distracted Driving Risk Prediction (DDRP) neural network by deep learning and spatio-temporal dependence is proposed, which to accurately predict the scale of distracted driving behavior on road networks. Then, the method is employed for distracted driving risk prediction based on the provincial road network. The experiment demonstrates that our method performs relatively better than the other methods applied in this paper. In addition, the method can adapt to predict the scale of distracted driving behavior in different categories, time intervals, and grid cells.

Evaluating road work site safety management: A case study of the Amman bus rapid transit project construction

Evaluating road work site safety management: A case study of the Amman bus rapid transit project construction

Study on the Safety Management of Connected and Autonomous Vehicle Test Roads Based on the Evaluation of Traffic Safety Facilities

More and more connected and autonomous vehicle (CAV) open test roads reconstructed on the basis of traditional roads have appeared in China. However, the management policies vary, which makes the traffic environment complicated. This paper takes CAV test road safety management as the research aim and investigates the open test condition through the evaluation of the traffic safety facilities. Indicators were rigorously screened, then the game theory model was used to determine the combination weight of the indicators, and the set pair analysis was applied to solve the uncertain problems. A case study for the CAV test road network of a city in central China was implemented and the results show, regarding the traffic safety facilities’ condition, among the 20 sections of the city’s CAV test road network, 15% of which are at an excellent level, 75% of which are at a good level and 10% of which are at a moderate level; road signs, guardrail facilities, isolation facilities and road features are the main limiting factors affecting the level of traffic safety facilities. Based on the results, recommendations have been made for the transport management authorities in the aspects of safety management policy-making and facilities maintenance.

Proposing a framework for evidence-based road safety policy-making: Connecting crash causation, countermeasures and policy

Effective evidence-based policy making within road safety is a several step cyclic process that involves gathering data about the causes of crashes, analysing these data, developing countermeasures and implementing and evaluating them. There are many examples of crash causation focused data collection activities available to policy makers but knowledge on how these finding may have led to countermeasure implementation and new policy is much less well established. This paper proposes a framework for best practice evidence-based policy making. To address existing gaps, the framework consists of three pillars: these are (1) Crash causation establishment; (2) Countermeasure development and implementation; and (3) road safety management. A key element in this framework is the recommendation for the establishment of an organisation responsible for road safety that has a strategic and coordination role. This framework, as a whole, aims to provide a practical high-level map by connecting evidence to policy at every point in the policy making cycle and ensuring that evidence-based road safety policy is a national priority. It is anticipated that using this framework to inform road safety policy development will enhance the success of any developed policy.

Road Safety for All Road Users – Current Situation and Perspectives for 2030

Abstract This article presents a brief overview of Romania’s road infrastructure and road safety situation in general, and outlines perspectives for reducing the number of lives lost in road crashes by 2030. Romania’s road fatality rate per 100,000 population has improved overall from a 2008 high of around 15 to the current 2020 value of 8,5. However, despite a 30,8% reduction in road deaths during the past decade, Romania’s total annual number of road deaths has remained at an average of around 1890 fatalities per annum over this period, and compared to the EU average of 36,7%, Romania is positioned among the worst performing countries in the European Union (EU) in recent years in terms of road safety. A World Bank road safety management capacity review performed in 2016 found a series of factors to be contributing to this high number of road deaths, such as inadequate political commitment to effective actions to reduce road fatalities, fragmented government road safety activities across a number of regulatory entities, speed limits set at levels that exceed internationally accepted survivable limits, weak traffic law enforcement including a lack of speed enforcement cameras resulting in a failure of drivers to comply with speed limits, and a lack of structured programs to implement human error tolerant road infrastructure constructed according to Safe System principles. A series of recommendations from the capacity review were adopted since 2016, although much remains to improve road safety in Romania, especially in light of the Second Decade of Action for Road Safety 2021-2030 and the United Nations Sustainable Goals.

Real-Time Traffic Sign Detection: A Case Study in a Santa Clara Suburban Neighborhood

This research project aims to develop a real-time traffic sign detection system using the YOLOv5 architecture and deploys it for efficient traffic sign recognition during a drive in a suburban neighborhood. The project's primary objectives are to train the YOLOv5 model on a diverse dataset of traffic sign images and deploy the model on a suitable hardware platform capable of real-time inference. The project will involve collecting a comprehensive dataset of traffic sign images. By leveraging the trained YOLOv5 model, the system will detect and classify traffic signs from a real-time camera on a dashboard inside a vehicle. The performance of the deployed system will be evaluated based on its accuracy in detecting traffic signs, real-time processing speed, and overall reliability. During a case study in a suburban neighborhood, the system demonstrated a notable 96% accuracy in detecting traffic signs. This research's findings have the potential to improve road safety and traffic management by providing timely and accurate real-time information about traffic signs and can pave the way for further research into autonomous driving.