Road Safety Assessment Research Articles

Risk Assessment of The Latifiya Expressway Segment Using a Proactive Approach

Road safety assessment is an essential step in the management of transportation projects. The proactive approach in the safety assessment considers the pre-construction stage to reduce the likelihood of road accidents. This research aims to assess the risk of road infrastructure in Iraq by applying the most recent methodology based on road features and supported traffic data. The selected methodology significantly contributes to enhancing a proactive approach to road safety and incorporating risk assessment in the planning and design stages of road projects. A segment of 15km from the beginning of the Latifiya expressway was selected for this study and assessed using the international road assessment program methodology. Data on road features and traffic were collected for this goal; where data on 38 aspects of road features is collected. The traffic data includes traffic volume and spot speed. Then these data were coded and processed using VIDA online web. The assessment results were presented for three road user groups: vehicle riders, pedestrians, and motorcyclists. The results showed that the assessed segment has a high-risk level; 1 to 3 stars for vehicle riders and 1 star for motorcyclists and pedestrians. Countermeasures were presented, and their efficiency was tested to raise the star rating for all road user groups to five stars.

ОСНОВНЫЕ ПРОБЛЕМЫ АВТОТРАНСПОРТНЫХ СООБШЕНИЙ НА СЕЛЕ

The article is devoted to the analysis of the main problems of existence and development of motor transport communications in rural areas. These include: poor quality of roads; insufficient car services, petrol stations and other infrastructure; lack of public transport; limited opportunities for development due to low population density; safety problems. It is proposed to consider the creation and development of "strongholds" of bus enterprises as one of the directions for ensuring convenient and regular transport in rural areas. Separately, the servicing of low-power passenger traffic, especially during hours of less congestion with individual transport (social taxi) and the creation and functioning of the system of guaranteed service to the population (GSP) of rural areas are highlighted. The system-wide aspects of motor transport communications influencing the development of rural areas are revealed: accessibility; economic development; social integration; development of engineering infrastructure; impact on the environment. All modern models of road safety assessment used in foreign and domestic practice are analyzed, it is noted that they are in the direction of convergence and harmonization. The tasks that need to be solved for the development of methods for assessing traffic safety levels in road transport communications in rural areas are identified. Scientific problems in improving road transport are identified, including improving engines (increasing efficiency, changing the design, new diagnostic methods, driving mode, creating new types of engines, etc.); increasing load-carrying capacity and passenger capacity (this is associated with improving roads, using new materials, changing design principles, reducing noise, improving the environment, searching for alternative types of fuel); environmental problems; safety problems.

USSC-YOLO: Enhanced Multi-Scale Road Crack Object Detection Algorithm for UAV Image.

Road crack detection is of paramount importance for ensuring vehicular traffic safety, and implementing traditional detection methods for cracks inevitably impedes the optimal functioning of traffic. In light of the above, we propose a USSC-YOLO-based target detection algorithm for unmanned aerial vehicle (UAV) road cracks based on machine vision. The algorithm aims to achieve the high-precision detection of road cracks at all scale levels. Compared with the original YOLOv5s, the main improvements to USSC-YOLO are the ShuffleNet V2 block, the coordinate attention (CA) mechanism, and the Swin Transformer. First, to address the problem of large network computational spending, we replace the backbone network of YOLOv5s with ShuffleNet V2 blocks, reducing computational overhead significantly. Next, to reduce the problems caused by the complex background interference, we introduce the CA attention mechanism into the backbone network, which reduces the missed and false detection rate. Finally, we integrate the Swin Transformer block at the end of the neck to enhance the detection accuracy for small target cracks. Experimental results on our self-constructed UAV near-far scene road crack i(UNFSRCI) dataset demonstrate that our model reduces the giga floating-point operations per second (GFLOPs) compared to YOLOv5s while achieving a 6.3% increase in mAP@50 and a 12% improvement in mAP@ [50:95]. This indicates that the model remains lightweight meanwhile providing excellent detection performance. In future work, we will assess road safety conditions based on these detection results to prioritize maintenance sequences for crack targets and facilitate further intelligent management.

Research on road crack segmentation based on deep convolution and transformer with multi-branch feature fusion

Abstract Efficient and precise identification of road pavement cracks contributes to better evaluation of road conditions. In practical road maintenance and safety assessment, traditional manual crack detection methods are time-consuming, physically demanding, and highly subjective. In addition, crack recognition based on image processing techniques lacks robustness. In this paper, a multi-branch feature fusion road crack segmentation network model (DTPC) based on deep convolution and transformer modules is proposed. The model is used for pixel-level segmentation of road crack images, which is a good solution to the existing needs and helps to repair dangerous cracks promptly in the follow-up work to prevent serious disasters due to crack breakage. Firstly, combine deep convolution with transformer modules to achieve precise local extraction and global contextual feature extraction. Secondly, a dual-channel attention mechanism is em-ployed to help the model better address information loss and positional offset issues. Finally, three-branch outputs are fused to obtain prediction maps that intuitively determine recognition results. The proposed model is tested for accuracy using a dedicated road pavement crack dataset. Results show that compared to mainstream models such as SegFormer, HRNet, PSPNet, and FCN, the DTPC model achieves the highest MIoU score (86.72%) and F1 score (92.49%).

Replication of a local record keeping method for collecting road crash data in low resource settings: lessons from Bangladesh and Nepal

BackgroundPolice road crash and injury data in low-income and middle-income countries are known to under-report crashes, fatalities and injuries, especially for vulnerable road users. Local record keepers, who are members...

Analyzing traffic conflict scenarios and safety assessment in the context of intelligent vehicles: A research review

The several internal disputes and various types of traffic accidents at an urban crossroads make for a complex traffic scenario. Because accident data and crash-based methodologies are limited, researchers are now examining other safety measures, including traffic conflicts that do not depend on crashes. To better assess road safety in the decades to come new real-time data is essential. The automobile industry is heading toward intelligent cars with the goal of enhancing road safety. Although there is a common failure mechanism between accidents and traffic conflicts during driving, which makes it possible to apply crash frequency and severity estimates to traffic conflict frequency and severity models, predicting traffic conflicts presents distinct difficulties. This study discusses conceptual and methodological concerns while reviewing research papers on traffic disputes as safety measures. It offers a thorough synopsis of previous research from the viewpoint of intelligent vehicles. It explains that there are three primary categories into which the perception technology of intelligent cars may be separated: perception technology, communication technology, and the combination of perception and communication technologies. The paper emphasizes the need of harmonizing international safety standards as well as the role that governments and regulatory agencies play in guaranteeing the security of intelligent vehicles. It highlights the need for additional research on transport conflict modeling techniques and offers practical recommendations for future study.

A comparison of traditional road safety assessment methods and the newly developed 'road safety deserts' approach

Road fatalities were labelled a pandemic as early as 1973 (BMJ 1973). The number of road fatalities reached 1.35 million in 2016. Currently over 3 500 people perish every day on the world's roads. South Africa has one of the highest road fatality rates in the world, with a fatality rate of 25.9 deaths per 100 000 population (WHO 2018). In order to understand and improve the road safety situation of a region, effective road safety assessments must be carried out. This paper presents a comparison of four different road safety assessment approaches, both traditional and novel, and serves as a proof of concept for the 'road safety desert' methodology, a new technique adapted from the 'transit desert' concept. This new approach to road safety assessment explores the possibility of geo-coded supply and demand comparisons to identify 'road safety deserts' - areas that have a comparatively higher road safety risk. This paper shows that there are several unique and effective ways to assess road safety, and that each approach incorporates different characteristics within their methodologies. It is recommended that road safety analysis is conducted using a multitude of methods, so as to improve understanding and intervention selection.

A Real-Time Machine Learning-Based Road Safety Monitoring and Assessment System

A Real-Time Machine Learning-Based Road Safety Monitoring and Assessment System

Mega-Events: Assessing Road Safety through an Operating Framework. An Application for the Milano–Cortina 2026 Winter Olympic Games

To meet the United Nations and European Union goals of reducing road crash fatalities and injuries, it is also relevant to address the negative externalities due to mega-events on the road network and the local communities, to assess the safety of the road network involved, and to implement appropriate measures for different road environments. Despite their relevance, the literature often overlooks social costs and risks associated with mega-events. This study presents an operating framework for rapidly assessing the safety of the Milano–Cortina 2026—“Via Olimpica” road—which will host a significant proportion of the traffic during the Winter Olympic Games in 2026. The framework proposes a simplified Road Infrastructure Safety Management (RISM) to address the unique challenges posed by the limited time available for screening and implementation by local authorities. The framework integrates four data sources and follows a seven-step procedure. It provides recommendations for improving road safety by identifying critical road sections and blackspots. Road authorities, practitioners, and public administrations may all benefit from the framework, as it makes it easier to prioritise safety improvements within time constraints.

Risk assessment of rear-end crashes by incorporating vehicular heterogeneity into Bayesian hierarchical extreme value models

Extreme value theory (EVT) has been extensively used to assess road safety with traffic conflicts. However, most studies used pooled models that do not account for vehicle heterogeneity which is characterised by different static and dynamic vehicle parameters such as size, speed, acceleration, and braking capacity. This study proposes a risk assessment technique for rear-end crashes while incorporating vehicular heterogeneity. Video-based trajectory data were collected at four uncontrolled intersections, and conflicts were estimated using modified time-to-collision (MTTC). The crash risk was derived from the observed conflicts using pooled as well as Bayesian hierarchical EVT models. Unlike the pooled model, the hierarchical model revealed that crash risk varies across leader-follower pairs. Interactions that involve cars or light commercial vehicles with slow-moving vehicles are riskier. This study highlights the importance of incorporating vehicular heterogeneity in crash risk assessment. The proposed methodology can be utilised for more accurate risk assessment in heterogeneous traffic.

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.

ПРЕДЛОЖЕНИЕ ПО СОВЕРШЕНСТВОВАНИЮ ПОДХОДА В ВОПРОСЕ ОЦЕНКИ БЕЗОПАСНОСТИ ДОРОЖНОГО ДВИЖЕНИЯ

This article deals with the issue of assessing road safety, from the point of view of the approach, namely, identifying the main places and causes of accidents on highways. The main methods of road safety assessment and their effectiveness based on statistics in the Russian Federation are analyzed. The main ideas of the concept of «Security II» are presented based on the experience of application in other fields of activity. A solution is proposed to shift the focus of attention in the assessment of road safety from critical cases to minor accidents and minor deviations. It is shown that the described approach contains the potential to reduce accidents and achieve zero mortality on the highway network.

Assessment of Road Safety Through Geometric Design Analysis: The Case of Sagonsongan Diversion Road in Marawi City

This study critically assessed the impact of geometric design on road safety. This assessment was based on a detailed analysis of the case of Sagonsongan Diversion Road in Marawi City, the Philippines. Applying quantitative methods such as a survey using Real-Time Kinematics and software-based road designing using AutoCAD Civil 3D, the study thoroughly analyzed the geometric design elements of the road, such as horizontal alignment, vertical alignment, and superelevation. Thus, the study showed that all geometric features were highly inconsistent with the safety standards. In particular, the high curve radius and slope aspect ratio significantly increase the risk of traffic incidents. The evaluation of two key curve sections has demonstrated that the road's overall geometric design directly affects drivers' ability to keep their cars stable. To eliminate the risk of traffic wretchedness, this study has suggested several corrective measures, including redesigning the road to adjust the geometrical aspects to safe norms, setting accurately calculated speed limits, reinstalling road signs, and conducting continuous safety checks. These measures will help to bring the Sagonsongan Diversion Road’s design to the level of established safety standards and decrease possible incidents to the minimum. The study has provided important findings on the relevance of established road designing principles to road safety. This work can be useful for the existing discussion regarding engineering and policy measures that should be considered to enhance road safety, especially in countries with developing economies. This study has also illustrated the importance of continued geometric design assessment in ensuring road safety and adapting the safety requirements to the dynamic needs of road users.

A Formal Approach to Road Safety Assessment Using Traffic Conflict Techniques

A Formal Approach to Road Safety Assessment Using Traffic Conflict Techniques

Dynamic Loss Balancing and Sequential Enhancement for Road-Safety Assessment and Traffic Scene Classification

Dynamic Loss Balancing and Sequential Enhancement for Road-Safety Assessment and Traffic Scene Classification

IMPROVING THE EFFICIENCY OF PROCESS MANAGEMENT IN THE FIELD OF TRAFFIC SAFETY ACCORDING TO ADDITIONAL CRITERIA

Known methods of assessing the level of traffic safety on highway sections are considered. When developing or improving road safety assessment methods, it is necessary to establish, among other things, the influence of variable road factors on traffic safety and to give a quantitative and qualitative assessment of the degree of their change. Factors that can significantly affect forecasting accuracy have been identified. All methods of detecting dangerous areas that are currently available are offered, based on traffic accident statistics. At different times, the following methods were proposed: analysis of statistical data by the methods of probability theory; use of multivariate correlation analysis data; analysis of traffic speed charts (methods of safety factors and "acceleration noise"); analysis using coefficients of relative influence of individual elements of the road (method of accident rates); method of conflict situations. 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 concentration of traffic accidents. It is proposed to evaluate traffic safety conditions by the final sum of points (on a 100-point scale), which takes into account a number of road construction characteristics. In our opinion, in conditions where the assessment of road safety conditions is entrusted to the representatives of the highway owner, this method looks more effective, since the road owner does not always or does not fully possess reliable statistical information about road accidents in the area being evaluated. The proposed method of evaluating the level of traffic safety is an undoubted step forward on the path of simplification and clarification existing methods. In the future, work on the construction of a simulation model of the interaction of the driver and the road as a subsystem of the general system "driver-car-road-environment" looks promising, by relying on criteria that were previously neglected - technical means of traffic management. This will make it possible to calibrate the proposed method and transfer its action to the software environment. In this way, the research will receive a significant platform for the involvement of additional variables - taking into account the road category, time of day, informativeness of technical means, their deformability, etc.

Application of artificial intelligence in road network inventory and network-wide road safety assessment

This research paper examines the application of artificial intelligence (AI) in road network inventory and network-wide road safety assessment. An overview of current research related to this field is presented and various aspects of the use of AI in road infrastructure and safety assessment are analysed. In today’s world, where the road network continues to develop and grow, the use of AI can offer significant advantages in the inventory and assessment of road infrastructure and safety. AI can be used to process large volumes of data collected from various sources such as geographic data, photos, documentation and more. One of the main aspects that the article looks at is the use of AI to automatically process data and create a digital inventory of the road network. AI systems can analyse geographic data and photos to identify and classify roads, road signs, markings, and other elements of road infrastructure. This can facilitate engineers and competent authorities in the process of road inventory and maintenance. Another important aspect discussed in the paper is the use of AI for the general assessment of road safety. AI models can analyse road infrastructure inventory data, as well as traffic data, crashes, and other factors, to provide a comprehensive assessment of road network safety. This allows the competent authorities to identify vulnerable places, carry out preventive measures and plan appropriate interventions aimed at improving road safety. In conclusion, the paper highlights the possibilities and advantages of using artificial intelligence in road network inventory and network-wide road safety assessment. Such an approach can contribute to more efficient management of road infrastructure and improvement of road safety.

WebGIS Based Road Crash Information System: A Case Study

Road crashes in India is showing progressive growth since COVID time, despite many road safety measures and program the rate of crashes are not declining. Many a times the road safety measures are not implemented in proper geographical locations owing to lack of proper crash information, which include crash information of the past. Road crash information is thus a vital support for the road safety assessment programs that eyes for a reduction in the road crashes. In India, as in other developing countries, very little effort is taken to providing enough road crash information. The identification of road crash location, analysis and treatment of road accident black spots are widely regarded as one of the most effective approaches to road accident prevention. A user friendly web Geographic Information System (GIS) based Road Crash Information System (RCIS) is developed in the present study for Kerala State, India. An online platform to add, update and maintain the database of road accident black spots is offered by the system, including analysis functionalities. Database maintains a standard guideline for road crash reporting thereby reducing data redundancy. Integration of all crash data from accident locations and to filter data based on different criteria are the core objectives of this study. The study also focused on systematically sharing the accident black spot details to the public user through an online platform.

Identifying risky driving behavior: a field study using instrumented vehicles

ABSTRACT Road crashes continue to be a leading cause of death globally, with most of these crashes reportedly occurring due to human factors. Traditional road safety assessment utilises geometric and traffic parameters that consider road design inadequacies and identify traffic conflicts. However, previous studies do not represent risky driving behavior and its influence on crash occurrence. Incorporating human factors into safety evaluation is crucial to enhance the prediction and subsequent prevention of unsafe events. This research establishes a methodology to identify risky driving behavior using driving performance measures. These measures are computed based on continuous driving profiles collected using instrumented vehicles from a sample set of drivers on an expressway and are compared with historical crash data. The results indicate the significance of driving performance measures in evaluating road safety. The performance measures find application in collision avoidance systems, assessing the road design quality, testing safety countermeasures and guide for policymakers..

Spatial analysis of road traffic crashes and user based assessment of road safety: A case study of Rotterdam

Objective To perform a spatial analysis of Road Traffic Crashes (RTCs) and assess road safety issues from the perspective of road users. Problem statement Although many initiatives have been taken to reduce the occurrence and severity of RTCs, they continue to persist. Existing research often investigates the spatial occurrence of RTCs or the perception of road safety issues from the road user. In doing this, only a limited number of factors that contribute to RTCs can be revealed, whereas in most RTC occurrences a multitude of factors plays a role. A more integrated approach combining both knowledge areas can contribute to improving road safety. Methods RTCs that occurred from 2018 to 2020 in Rotterdam, in the Netherlands, were spatially analyzed. This was performed using Network Kernel Density Estimation (NKDE) analysis. Two zones within the study area were selected to understand road users’ perceptions of road safety through a survey. Furthermore, opinions toward possible recommendations for improving road safety were also collected through key informant interviews. Results NKDE resulted in a hot-spot map of the road segments in the study area that showed the frequency of RTCs using different colors. The road segments were classified based on the number of RTCs from 2018 to 2020, ranging from zero to 17.9 RTCs per kilometer. This led to the selection of a hot and cold spot zone for further analysis. The road user perception survey resulted in the discovery of qualitative responses that can be used to improve road safety in future and the possible recommendations would be well received by them. The key-informant interviews acted as a backup to the opinions given by the road users and provided insights on what is being done in the study area to improve road safety. Conclusion The synthesis of findings unveiled why road users perceive some areas as dangerous and which road policies need to be revised to improve road safety in Rotterdam.