Road Safety Management System Research Articles

An adapted taxonomy and framework for monitoring road safety strategies: a case study of Morocco.

Traditional approaches to monitoring road safety have primarily focused on measuring outcomes such as the number of fatalities and injuries. While effective in capturing overall trends, this macroscopic approach often overlooks the underlying causes of unsafe conditions. Recognizing these limitations, many countries now embrace a safe system-based approach, which emphasizes a holistic view of road safety, considering various elements and their interactions. In response to this shift, this study introduces a five-step framework designed to provide comprehensive coverage and tailored assistance in selecting and utilizing appropriate Road Safety Indicators (RSIs) for more effective performance monitoring. The framework integrates a novel RSIs taxonomy aligned with critical elements of the safe system. It also incorporates an MCDA-based approach to account for decision-makers' preferences when selecting suitable RSIs. A case study demonstrates the practical application of the proposed steps, including the identification, classification, selection, and development of descriptive sheets for each selected RSI, as well as the continuous updating of the RSIs set. The findings offered valuable insights into the commonly used indicators in international road safety reports, while also revealing the limitations of currents metrics and data in fully capturing critical elements and hierarchical level within the road safety management system.

The Characteristics of Road Crashes on Indonesian National Roads Based on Integrated Road Safety Management System Data

The Characteristics of Road Crashes on Indonesian National Roads Based on Integrated Road Safety Management System Data

Investigation on lightweight identification method for pavement cracks

Mastering a lightweight method for the identification of pavement surface cracks plays a vital role in enhancing the efficiency and accuracy of pavement crack detection. However, the existing crack detection methods typically concentrate on improving model performance while neglecting the study of lightweight identification methods, causing inefficient crack detection. Therefore, this study aims to investigate a lightweight identification method for pavement cracks. High-definition vehicle-mounted cameras were employed to collect pavement crack images across multiple districts in Shenzhen, China, resulting in a dataset containing 20256 images. In addition, a pavement crack detection method based on the You Only Look Once version 8 (YOLO v8) model was proposed. On this basis, a knowledge distillation model enhanced by multiple teacher-assistants (KDMTA) was established to explore the impact of different teacher assistants and distillation paths on model performance. The results indicate that in the proposed KDMTA model, each level of teacher-assistant (TA) model can absorb diverse aspects of knowledge from the teacher models, thus broadening the range of insights. Subsequently, the information is imparted to lower-level TA models and the student model, expanding the perspective from which the student model acquires knowledge. Therefore, this improves the model's generalization performance and its effectiveness in target detection. The type and number of teacher assistant models, as well as the distillation path, influence the model performance. The random learning strategy enriches the combination and transfer of knowledge among teacher models, TA models, and student models, achieving lightweight identification for pavement crack. According to the results, the crack identification accuracy reaches 95.79 %, the mAP reaches 81.07 %, and the image processing speed has been improved by 79.6 %. This study enhances the efficiency and accuracy of pavement crack detection, providing methodological support for the rapid and accurate identification of pavement cracks. This study offers a novel method for the rapid and accurate identification of pavement cracks, which can assist management authorities to timely identify and repair pavement defects, reducing maintenance costs, and extending the lifespan of roads. Moreover, it can also facilitate the development of road safety management and intelligent transportation systems.

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.

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.

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.

Study of quality models used by logistics operators in Argentina and South America

This research aims to gather information on the use of Excellence models (such as the Quality Awards) by Logistics Operators in Argentina and neighboring countries. Therefore, the object of the research is the quality models used by logistics operators. In general, these organizations, and mainly those where Logistics is their main service, have begun applying quality standards later than other types of organizations, like those in the manufacture or services industries. This is because their practice is not deemed an added-value activity by the customer. This study found that, at the regional level, these companies are in the process of applying Quality, Environmental, Road Safety and other Management Systems from the ISO suite (ISO 9001, ISO 14001, ISO 39001, etc.), but the use of Excellence models is only present in some, mainly in Argentina-based companies. Remarkably, this behavior does not replicate in other countries of the region (Brazil, Chile, Uruguay), where the aforementioned standards are more widely applied. On the other hand, some countries have their own standards (Argentina, Brazil) for Freight activities developed by their local chambers, but these are mainly focused on the operational aspect of their activity. In this study, information was collected regarding the methodology related to quality and excellence of 109 companies, belonging to Argentina, Brazil, Chile, Colombia and Peru. At the regional level, the ISO 9001 management model is adopted by 68 % of the organizations studied, followed by ISO 14001 adopted by 30 % of the organizations and ISO 45001 adopted by 15 % of the organizations. Based on the conclusions of this work, there is an opportunity to create specific policy around Quality Models for this industry that will support the creation of new standards and the improvement of existing ones. This paper can serve as a starting point for analyzing the scenario in other regions within a framework of similar geographical characteristics.

Improvement of road safety management systems of local governments in Korea after evaluating related indicators

This study analyzes what has changed in the road safety management systems (RSMS) of local governments after evaluating the related indicators continuously from 2015 to 2019. It has evaluated 24 indicators in four areas of RSMS such as institutional management, intervention, intermediate outcome, and the final outcome, according to Han and Lee (2020). It seems that continuous evaluation of indicators of RSMSs in local governments for five years has helped to improve road safety performance in local governments. The result shows that the average overall scores have increased from 77.92 to 83.89, a 7.7% increase. This improvement should have resulted from the competition between local governments, which do not want to be at the bottom in the score comparison. The most remarkable increase has been identified in the areas of institutional management and intervention. They have increased by 13.3% and 16.0%, respectively. In terms of types of local governments, it has been found that the increase in overall scores is higher in provinces which include most rural areas than in metropolitans. However, it has been found that the improvement of institutional management and interventions are not directly associated with the increase of the intermediate and final outcomes, at least during the five years. Further studies examining the relationship between the four areas of RSMS are needed.

Development of a City-wide Traffic Accident Prediction Model Using Hybrid Machine-based Learning Approaches

Predicting traffic accidents is a challenging task because taking into account uncertainty in modeling traffic accidents is not trivial. To address these issues, this article develops a hybrid modeling pipeline combining unsupervised and supervised learning to predict the level of hazardous road sites and explore the causality of accidents by controlling unobserved heterogeneity issues effectively. Traffic accident data for Won-ju province, Korea, from 2020 to 2021, and external factors affecting traffic accidents, such as average travel speed and weather information, are combined based on road links. Through the modeling pipeline, a clustering technique is adopted to capture unobserved heterogeneous information among roads. Since traffic accident data contains a wide variety of categorical and hierarchical features, ensemble methods such as boosting techniques were applied to handle heterogeneity issues among these features. To explore the relationship between the accident and determinant factors, are adopted to interpret the results of machine learning models. Model-agnostic methods, however, generally provide results based on images, this study also added a process that extracts texts from images to overcome compatible issues with existing road safety management systems.

Analysis of Traffic Accidents Using the Accident Rate Method (Case Study: MT. Haryono Street – Gatot Subroto Street South Jakarta City)

MT. Haryono - Gatot Subroto Street South Jakarta is one of the arterial roads in the Special Capital Region of Jakarta that connects three cities, South Jakarta City and East Jakarta City. Based on IRSMS (Integrated Road Safety Management System), data from the National Police showed increased traffic accident cases in MT. Haryono Gatot Subroto Street South Jakarta, where in 2019, there were 21 traffic accident cases. In 2020, there were 30 traffic accident cases, and in 2021, there were 38 traffic accident cases. This research method was descriptive, which describes current conditions using the Accident Rate method. This study aims to determine the level of traffic accidents on MT Haryono - Gatot Subroto Street, South Jakarta, to know the factors that cause accidents, and to find the black site area on MT. Haryono - Gatot Subroto Street South Jakarta, and to provide alternatives and solutions for prevention and management in reducing traffic accidents. This research shows that the black site area was on Gatot Subroto Street Segment 6, KM 5+500 – KM 6+500, with an accident rate of 6.33. The Blackspot identification was based on an accident rate of 0.27, and the black site identification was based on an accident rate of 0.27. The highest time of occurrence of traffic accidents was on Sunday at 03.00 – 04.00 WIB, so an actual speed survey was implemented at that time. The primary cause of traffic accidents based on POLRI IRSMS data was the human factor as drivers who are negligent and undisciplined in driving. An example of the causal factors found during the survey was drivers driving over the maximum speed limit—the alternatives and solutions for prevention and countermeasures in reducing traffic accidents on MT. Haryono-Gatot Subroto Street South Jakarta provides adequate lighting along the way and adds traffic signs before black site areas.

Different Forecasting Model Comparison for Near Future Crash Prediction

A traffic crash is becoming one of the major factors that leads to unexpected death in the world. Short window traffic crash prediction in the near future is becoming more pragmatic with the advancements in the fields of artificial intelligence and traffic sensor technology. Short window traffic prediction can monitor traffic in real time, identify unsafe traffic dynamics, and implement suitable interventions for traffic conflicts. Crash prediction being an important component of intelligent traffic systems, it plays a crucial role in the development of proactive road safety management systems. Some near future crash prediction models were put forward in recent years; further improvements need to be implemented for actual applications. This paper utilizes traffic accident data from the study Freeway in China to build a time series-based count data model for daily crash prediction. Lane traffic flow, weather information, vehicle speed, and truck to car ratio were extracted from the deployment of non-intrusive detection systems with support of the Bridge Management Administration study and were input into the model as independent variables. Different types of prediction models in machine learning and time series forecasting methods such as boosting, ARIMA, time-series count data model, etc. are compared within the paper. Results show that integrating time series with a count data model can capture traffic accident features and account for the temporal structure for variable serial correlation. A prediction error of 0.7 was achieved according to Root Mean Squared Deviation.

Оптимізація управлінських підходів для впровадження та ефективності системи менеджменту безпеки дорожнього руху

The issues of optimizing management approaches for effective implementation of road traffic safety management systems in enterprises are considered. The authors examine internal and external factors affecting road safety and explore methods to enhance management approaches to ensure a high level of safety. Special attention is paid to process and risk-oriented approaches, as well as the importance of interaction with stakeholders. It is noted that a successful safety management strategy involves integrating safety into all areas of enterprise activities and continuously monitoring the system. Research confirms that adopting such a risk-oriented approach significantly enhances the effectiveness of road safety management systems. Reduction of risks and improvement of safety are the outcomes of implementing this strategy, enabling enterprises to more efficiently control and manage potential road hazards. Future research plans include focusing on developing new methods to assess the effectiveness of safety management systems, aimed at further improving the functionality and reliability of such systems. Additionally, there are plans to conduct a detailed study on the impact of innovative technologies on the functioning of road safety systems, to implement the most effective technical solutions. There are also plans to expand the application of safety management systems across various types of enterprises to ensure an optimal level of safety for a wide spectrum of road users. This may include not only transportation companies but also construction, logistics, and other industries involved in the transportation of goods and services. Taking into account the prospects of further research, it is expected that they will contribute to further improving road safety and ensuring stability for all road users, which is a crucial step in providing safety and comfort on highways.

Operational Data Analytics of Over Dimensional and Overloaded Truck in Indonesia

Over Dimension and Overload (ODOL) vehicles are one of the factors that cause road conditions to be easily damaged and potholes and can cause the risk of traffic accidents. ODOL vehicles are considered very detrimental to road infrastructure and increase the risk of accidents, inefficiency due to damaged road conditions, and air pollution because of excess exhaust gases. Road damage due to ODOL also triggered an increase in the budget for the maintenance of national roads, toll roads, and provincial roads, with an average of Rp 43.45 T per year. Based on data from the National Police Corps from the Integrated Road Safety Management System (IRSMS) regarding accidents in 2018, ODOL trucks are one of the biggest contributors to traffic accidents. This study aims to determine the causes of vehicle overload and over dimension. The method used is to use the triangulation method. Triangulation of techniques means that researchers use different data collection techniques to obtain data from the same source. The results showed a relationship between vehicle modification and ODOL violations which were proven using Chi-square analysis. The chi-square value is 55,259 with a p-value of 0.000 at a significance level of 1%. This shows a significant relationship between vehicle modification and Over Dimension and Overload (ODOL) vehicle violations.

The applicability of Smeed’s Law in an evaluation of national road safety management systems: Montenegro versus Great Britain

The need for an evaluation and international comparison of the effectiveness of national road safety management systems (NRSMSs) is growing, since international goals aim to reduce road deaths from decade to decade, which should be achieved at the national level. In addition, an NRSMS evaluation model needs to be as simple as possible to be applicable in all countries of the world. To this end, an NRSMS evaluation model was generated in this study based on Smeed’s Law, which used population, vehicles, and road deaths and applied the definite integral to the following hypothesis: The effectiveness of an NRSMS is equal to the difference between the adjusted Smeed’s Law assessment of road safety and the current road safety condition achieved by implementing the NRSMS. Applying this model to Montenegro and Great Britain, it was found that Smeed’s Law could be used to assess the effectiveness of NRSMSs with respect to the turning point from an increasing to a decreasing number of road deaths.

Entropy Method of Road Safety Management: Case Study of the Russian Federation.

Within the framework of this paper, the author’s entropy method of road safety management in large-sized systems is considered. The road safety management system in the Russian Federation, the largest country in the world, was selected for this case study. The purpose of the article is to present the opportunities and methodology of the use of quantitative assessments of the orderliness of the road accident rate formation process in regional transport systems for road safety management. Orderliness, in other words, systemic anti-chaos, can be quantified using the C. Shannon informational entropy H. The article consists of the results of the issue’s state analysis; methodology of assessment of the orderliness of the road accident rate formation process based on the using of the cause-and-effect chain; entropic method of the road safety management in large-scale systems, in particular, the algorithm of management of regional road safety in Russia taking into account the level of its entropic orderliness; and examples of the quantitative evaluation of the orderliness of regional road safety provision systems in Russia. The key results of the research are spatio-temporal patterns of the change of the orderliness of the road safety provision systems in the Russian Federation in 2004–2020. Based on the results, conclusions and recommendations about the practical application of the entropic method of road safety management in large federal states with complex administrative structures were formulated. These results give an idea of the possibilities of the usage of entropic approaches in road safety management to assess the orderliness of the regional transport systems and the advantages of the entropic method over other managerial methods.

THE IMPLEMENTATION OF INTEGRATED ROAD SAFETY MANAGEMENT SYSTEM IN JAKARTA, INDONESIA

The aim of this research was to know how the Integrated Road Safety Management System was applied in Jakarta. It was predicted that more than ten percent of traffic accidents had caused fatalities and most of the victims in Jakarta were bikers. The research method used was a qualitative method using judgment samples or purposive sampling used for selected informants in helping researchers answer research questions in case studies. The research instrument used was the Strength-WeaknessOpportunity-Threat analysis toward the road safety management system and transportation sector as well as the feasibility study of public road space redistribution and the traffic flow in downtown of Jakarta. The results of this study were expected to result from a national action plan as part of a new transportation policy that is formed in everyday life as self-reflection, mutual learning, active participation and empowerment. The results of this study were expected to produce public safety service products in the form of policies, Standard Operation Procedures, and education to the public of Jakarta. Just standard design could not guarantee road safety in all conditions, so it needed to further evaluation and research to measure the impact of a planning decision on safety.

Understanding the potential of emerging digital technologies for improving road safety

Each year, 1.35 million people are killed on the world’s roads and another 20–50 million are seriously injured. Morbidity or serious injury from road traffic collisions is estimated to increase to 265 million people between 2015 and 2030. Current road safety management systems rely heavily on manual data collection, visual inspection and subjective expert judgment for their effectiveness, which is costly, time-consuming, and sometimes ineffective due to under-reporting and the poor quality of the data. A range of innovations offers the potential to provide more comprehensive and effective data collection and analysis to improve road safety. However, there has been no systematic analysis of this evidence base. To this end, this paper provides a systematic review of the state of the art. It identifies that digital technologies - Artificial Intelligence (AI), Machine-Learning, Image-Processing, Internet-of-Things (IoT), Smartphone applications, Geographic Information System (GIS), Global Positioning System (GPS), Drones, Social Media, Virtual-reality, Simulator, Radar, Sensor, Big Data – provide useful means for identifying and providing information on road safety factors including road user behaviour, road characteristics and operational environment. Moreover, the results show that digital technologies such as AI, Image processing and IoT have been widely applied to enhance road safety, due to their ability to automatically capture and analyse data while preventing the possibility of human error. However, a key gap in the literature remains their effectiveness in real-world environments. This limits their potential to be utilised by policymakers and practitioners.

LOCATION OF SPEED CONTROL CAMERAS ON HIGHWAYS: A GEOSPATIAL ANALYSIS

This research proposes a methodology to identify critical sections of highways where the location of speeding control may be beneficial. The method relies on a spatial and statistical analysis of infrastructure risks, along with traffic accident frequency and severity. A relevant feature of this methodology is related to its potential to be used in areas where there are no detailed historical records about traffic crashes, which is common in Global South countries. We applied the methodology to a rural road network in Colombia, where a recent law established that technical criteria should support the location of speed cameras. The case study uses accident information from six years, and risk data from a road safety audit carried out in the area under study. Even though historical records of accidents in the area were not fully available, the methodology allowed prioritising speed camera installations in the zone and identifying the relevant variables to define camera location. The relevant variables were the geometric characteristics of the road, traffic flows, risk factors, and proximity to populated centres. The use of speed controls should be part of a road safety management system, which allows defining camera location according to robust technical criteria.

Selected Aspects of the Road Traffic Safety Management System

The article is an attempt to present the two modern concepts of safety management systems. The first is the sunflower pyramid used especially in Europe, the second is the ISO 39001 standard (Road Safety Management System) of international scope. The idea and possibilities offered by use of both concepts of safety management, as well as an attempt to assess the impact of their actions on decrease in fatalities in selected EU countries, were presented. As a result of the conducted analyses, a downward trend was noted as to the number of certificates issued for compliance with the ISO 39001 standard in the world, which may indicate an appearance of the more adequate management systems, or other tools or concepts to improve the road safety, or inadequate education society and low level of awareness in this area of stakeholders, including politicians, scientists, producers, drivers (professional and reliable), as well as other road users, including unprotected ones.