Road Transport Safety Research Articles

THE IMPACT OF ROADSIDE TECHNICAL INSPECTIONS ON TRANSPORT AND LOGISTICS SYSTEMS IN THE SLOVAK REPUBLIC

Road transport safety, apart from environmental protection, is one of the most important tasks for the contemporary world. Annually, about 50 million people are injured in road accidents around the world, of which nearly 1.5 million die as a result. Transport safety consists of many activities and includes various initiatives, including legal changes, training, preventive actions, building safe road infrastructure, and the production of safe vehicles and control systems. This article deals with the issue of performing roadside technical inspections in the Slovak Republic. Specifically, it analyses the results of these roadside technical inspections in individual counties of the Slovak Republic and examines their possible impact on transport and logistics systems in these counties. The correlation analysis showed that the number of checks under the technical roadside inspections is weakly related to the number of logistics centres, but the number of such inspections is moderate in relation to the number of failed vehicles (correlation coefficient = 0.44079). In addition, due to the age of the vehicles, the number of serious and dangerous faults found on them during maintenance inspection increases.

Application of Feature Selection Approaches for Prioritizing and Evaluating the Potential Factors for Safety Management in Transportation Systems

Road safety assessment is one of the most important parts of road transport safety management. When road transportation networks are managed safely, they improve the quality of life for citizens and the economy as a whole. On the one hand, there are many factors that affect road safety. On the other hand, this issue is a dynamic problem, which means that it is always changing. So, there is a dire need for a thorough evaluation of road safety to deal with complex and uncertain problems. For this purpose, two machine learning methods called “feature selection algorithms” are used. These algorithms include a combination of artificial neural network (ANN) with the particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm. In this study, two data sets with 202 and 564 accident cases from cities and rural areas in southern Italy are investigated and analyzed based on several factors that affect transportation safety, such as light conditions, weekday, type of accident, location, speed limit, average speed, and annual average daily traffic. When the performance and results of the two models were compared, the results showed that the two models made the same choices. In rural areas, the type of accident and the location were chosen as the highest and lowest priorities, respectively. According to the results, useful suggestions regarding the improvement of road safety on urban and rural roads were provided. The average speed and location were considered the highest and lowest priorities in urban areas, respectively. Finally, there was not a big difference between the results of the two algorithms in terms of how well the algorithm models worked, but the proposed PSO model converged more quickly than the proposed DE model.

China’s Road Traffic Mortality Rate and Its Empirical Research from Socio-Economic Factors Based on the Tobit Model

Road transport safety is an important part of transport construction in China. China is now the world’s second-largest country for road traffic deaths. Research on the road traffic mortality rate (RTMR) in China is of great significance in promoting sustainable development in global traffic. This study analyzes the RTMR in 31 provinces in China between 2003 and 2018. Research shows that the RTMR of China demonstrated a downward trend after 2004, but it increased slightly after reaching the lowest points in 2015. The RTMR in coastal and western areas was quite high, requiring targeted management and prevention. During the study period, the RTMR in Guangdong and Tibet improved greatly, whereas the RTMR in Hubei and Guangxi deteriorated. Tobit model results show that economic development level, medical assistance level and government expenditure on health are significantly negatively correlated with RTMR, while urbanization level and motorization level significantly promote RTMR. This study provides macro policy support for improving traffic safety in China.

Utilizing the Harvesting of Rainwater to Provide Safe Road Transportation Efficiency and Increase Water Resources in the Context of Climatic Change

This research investigates the effect of heavy rain on highway traffic volume and average speed, and proposes a recharging well harvesting system as an alternative freshwater source in the context of climate change. The Cairo Autostorad highway was taken as a case study. The highway climate data were collected, and traffic was measured using Metrocount equipment during the period from 2008 to 2020. The results show that the studied road is about 12 km long, and about 40 water ponds exist along the route. Each pond has an estimated water volume of 300 m3, and a 30 cm recharging well, with a maximum recharging capacity of 25 m3/h with satisfactory performance, is recommended to be constructed for rainwater harvesting. The recharging wells will clear the ponding volume within 2.5 to 3.5 h after the rainfall has stopped. The design incorporates a 1.2 safety factor against blockage inside the well. In addition, a model was established between the average rainfall depth and the average measured highway speed for the period (2008–2020) during rainy months, indicating an exponential function with a determination factor R2 = 0.7076. The present rainfall (2020) and the representative concentration path (RCP) for 4.5 and 8.5 emissions scenarios were used to simulate the rainfall for future years: the 2040s, 2060s, 2080s, and 2100s. The results show that in the winter season for the current scenario (2020), the average rainfall depth was 45 mm, and the highway speed was 78 km/h. For the RCP 4.5 emission scenarios for the 2040s, 2060s, 2080s, and 2100s, the rainfall depths were 67.8, 126.4, 131.2, and 143.9 mm, and the corresponding reductions in the highway speeds were 23, 34, 35.3, and 36.9%, respectively, compared to the baseline scenario (2020). On the other hand, the RCP 8.5 emission scenarios show a reduction in the highway speed of 23, 34.5, 36.9, and 36.9% for the years 2040, 2060, 2080, and 2100, respectively, due to rainfall depths of 68.7, 128.4, 143.9, and 143.9 mm, respectively. This study helps policymakers to make wise decisions regarding sustainable water resource management and highway traffic problems related to rainwater depths in the context of climate change.

Elite Vehicle Controller: Drunk Driving Detection with Vehicle Ignition Locking Using GSM

Abstract— Alcohol detection system is developed for road transportation safety in smart city using Internet of Things (IoT) technology. Due to the rapid increase of vehicles on roads, the probability of road accidents is rising steeply. Drunk driving is considered to be a major cause of road accidents throughout the world. The main aim of this project is to develop a system that would detect the amount of alcohol that is consumed by the driver of the vehicle. This system discusses development and live-performance test of the prototype of drink and drive situation detection and alert cum vehicle control system to minimize road mishaps and enhance public safety on road. The model is developed using Arduino Uno and alcohol detection sensor (MQ-3) as its major components. As a safety measure, when the level of alcohol crosses a permissible limit, Vehicle Control System is triggered to lock ignition or stop the fuel inflow to the vehicle. Additionally, ‘alert SMS’ indicating drunk driver location, tracked by onboard GPS receiver, along with vehicle number is communicated remotely to the authorized people (family). The deployment of this system will help in reducing the incidence of drunk driving related road accidents in smart cities. Keywords—GSM, GPS, Alcohol sensor, Arduino microcontroller, ignition control (DC Motor), Buzzer

Functional model of road safety for motor transport enterprise with account of high environmental temperatures

Introduction. The accident rate in road transport in the summer period of the year in conditions of high ambient temperatures has a steady upward trend. The main reasons for this situation are changes in the condition of drivers and the low efficiency of the climate systems of some vehicles. Motor transport enterprises take into account the conditions of the external environment in their activities and adjust the technology of the transport process. Such environmental conditions are ice, fog, precipitation, low air temperature and others. However, high ambient temperature is not included in such a list. In this regard, the study of the features of the functioning of a motor transport enterprise in conditions of high ambient temperatures and the development on this basis of a functional model for ensuring road safety is an urgent task.The purpose of this work is to improve the safety of road transport on the basis of the development of a functional structure of the road safety service of a motor transport enterprise, taking into account the high ambient temperature.Methods. The work is based on the synthesis of general scientific research methods, which included the provisions of system analysis, as well as the provisions of the theory of transport processes, the technical operation of vehicles.Results. The result of the work was the developed functional model of the road safety service of a motor transport enterprise, according to which the road safety service is endowed with control and administrative functions in relation to the activities of the personnel service, technical service and operation service on issues related to road safety. This model differs from the known ones in that it contains functions for responding to high ambient temperatures. An algorithm has been developed for the functioning of the traffic safety service in conditions of high ambient temperature, which prevents its influence on the increase in the aggressiveness of driving cars and the low efficiency of the climate systems of a part of vehicles. These provisions constitute the scientific novelty of the work.Discussion and conclusion. The practical value of the work lies in the improvement of the tools of the motor transport enterprise, aimed at preventing an increase in the accident rate in conditions of high ambient temperatures.

Ensuring the Safety of Passenger Road Transportation as a Subject of Legal Regulation and an Object of Forensic Scientific Knowledge

This article establishes an understanding of safety of passenger road transport as a subject of legal regulation and an object of forensic scientific knowledge. The purpose of our study was to create provisions for ensuring the safety of passenger road transport. The research methodology was based on a theoretical analysis of the main scientific provisions for the disclosure, investigation and prevention of road traffic crimes. The information and empirical basis of the study was statistical data and analysis of law enforcement practices. The object of the study was data on traffic accidents involving intercity buses. The scientific novelty is the development of the author's conceptual approach to the issues of ensuring safety of passenger road transportation. The study showed that at present there is no single approach or a concept for the investigation of such crimes. There are many studies on certain aspects of disclosure, investigation and prevention of road traffic crimes, however, they only indirectly analyzed road traffic crimes in which intercity buses were involved. Among other things, the study provides a list of legal acts necessary to investigate crimes in the field of violations of the safety of passenger road transport in order to determine the guilt of participants in road accidents and people subject to criminal liability.

DRIVER TIREDNESS DETERMINATION METHOD

DRIVER TIREDNESS DETERMINATION METHOD Vigen Poghosyan Master student, National Polytechnical University of Armenia, Republic of Armenia, Yerevan   ABSTRACT The safety of road transport, for every state, including the Republic o

Integrating user Psychology in Road Transport Safety: A Model for Low and Middle-Income Countries, Based on Sri Lanka

Low and Middle Income Countries (LMICs) account for 90% of road collisions that occur globally. These collisions account for 17.63 million serious injuries and deaths while costing LMIC economies 1.7 trillion USD amounting to 6.5% of aggregate annual GDP. Though there are a plethora of models on road transport safety, few address the impact of user psychology and the contextual environment in LMICs on road safety. This paper proposes such a model formulated and developed using existing literature and focus group discussions. The model is composed of three key constituents - behaviour, skills and knowledge, and infrastructure. It further interprets behaviour as an integration of three sub-elements—mindset, circumstances and legal system—pertaining to the incident. The interaction between these constructs constitutes user behaviour in a given circumstance. The research also proposes an implementation guideline to authorities that is instrumental in changing society’s perception of transport safety.

Game Theory-Based Modeling of Multi-Vehicle/Multi-Pedestrian Interaction at Unsignalized Crosswalks

<div class="section abstract"><div class="htmlview paragraph">The improvement of road transport safety requires the development of advanced vehicle safety systems, whose development could be facilitated by using complex interaction models of different road users. To this end, this paper deals with the modeling of multi-vehicle/multi-pedestrian interactions at unsignalized crosswalks. This multi-agent modeling approach extends on the existing basic model covering only single-vehicle/single-pedestrian interactions. The basic model structure and parameters have remained the same, as it was previously experimentally calibrated and thoroughly verified. The proposed modeling procedure employs the basic model within the multi-agent setting based on its application to relevant single-vehicle and single-pedestrian pairs. The resulting, so-called pre-decisions are then used for making final crossing decisions in a current time step for each agent. During the decision making process, each pedestrian takes into account approaching vehicles and their expected future behavior with respect to all pedestrians (i.e., himself/herself and other pedestrians), and vice versa for vehicles. This generalized multi-agent model is conceived to support simulation scenarios concerning multi-lane roads and pedestrians approaching from both sides of a road, as well. The model is verified by means of large-scale simulations and various scenarios.</div></div>

Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data.

Although crashes involving hazardous materials (HAZMAT) are rare events compared with other types of traffic crashes, they often cause tremendous loss of life and property, as well as severe hazards to the environment and public safety. Using five-year (2013–2017) crash data (N = 1610) from the Highway Safety Information System database, a two-step machine learning-based approach was proposed to investigate and quantify the statistical relationship between three HAZMAT crash severity outcomes (fatal and severe injury, injury, and no injury) and contributing factors, including the driver, road, vehicle, crash, and environmental characteristics. Random forest ranked the importance of risk factors, and then Bayesian networks were developed to provide probabilistic inference. The results show that fatal and severe HAZMAT crashes are closely associated with younger drivers (age less than 25), driver fatigue, violation, distraction, special roadway locations (such as intersections, ramps, and bridges), higher speed limits (over 66 mph), midnight and early morning (12:00–5:59 a.m.), head-on crashes, more than four vehicles, fire/explosion/spill, poor lighting conditions, and adverse weather conditions. The overall prediction accuracy of 85.8% suggests the effectiveness of the proposed method. This study extends machine learning applications in a HAZMAT crash analysis, which would help develop targeted countermeasures and strategies to improve HAZMAT road transportation safety.

New Deal in Road Safety: Why we need NGOs

Road safety has been receiving increasing global attention, for example: the inclusion of road safety in the 2030 Agenda for Sustainable Development and the United Nations General Assembly (UNGA) adoption of resolution 74/299 “Improving global road safety”; Stockholm Declaration (2020) reflecting the recommendations of the Academic Expert Group of the 3rd Global Ministerial Conference on Road Safety. Recognising that the target to reduce road deaths and serious injuries by 50% was not met in the First Decade of Action for Road Safety (Decade of Action) 2011–2020, the UN High-level Political Forum on Sustainable Development made a pledge in September 2019 to make the new decade one of action and delivery. On 28 October 2021, the Global Plan for the Decade of Action for Road Safety 2021–2030 (the Global Plan) was launched to guide Member States in the implementation of key actions to improve road safety with the target to reduce road traffic deaths and injuries by at least 50% by 2030 (WHO & UN Regional Commissions, 2021). The Global Plan calls on governmental agencies as the primary responsibility holder in delivering a safe road transport system and implementing the required road safety actions to achieve the global target of 50% reduction in deaths and injuries by 2030. The Global Plan also calls on other actors — nongovernment organisations (NGOs), academia, youth, funders, private sector and UN agencies — as important contributors. NGOs are groups organised independently from governments at a local, national or international level to serve a certain cause such as road safety. NGOs exist to drive actions and bring improvements on a societal issue, and, in the field of road safety, NGOs drive road safety actions to protect people from being killed or seriously injured in our ubiquitous use of the road transport system.

A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data.

Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensors installed on the vehicle can be identified by the algorithm to obtain the distracted driving behavior data, which can be used to predict the driving behavior risk of the vehicle and the area. In this paper, a new neural network named Driving Behavior Risk Prediction Neural Network (DBRPNN) is developed for prediction based on the distracted driving behavior data. The network consists of three modules: the Feature Processing Module, the Memory Module, and the Prediction Module. In this process, attribute data (time in a day, daily driving time, and daily driving mileage) that can reflect external factors and driver statuses, are added to the network to increase the accuracy of the model. We predicted the driving behavior risk of different objects (Vehicle and Area). For the applicability improvement of the model, we further classify the distracted driving behavior categories, and DBRPNN can provide more accurate risk prediction. The results show that compared with traditional models (Classification and Regression Tree, Support Vector Machines, Recurrent Neural Network, and Long Short-Term Memory), DBRPNN has better prediction performance. The method proposed in this paper has been fully verified and may be transplanted into active safety early warning system for more accurate and flexible application.

ANALYSIS OF METHODS FOR DETERMINING DRIVER FATIGUE AND A WAYS OF THEIR IMPROVEMENT

The safety of road transport is the main component of the security system for each state. The low level of road safety has a significant impact on the safety of the community. Studies have been carried out on the etiology of road traffic accidents. They showed that as a result of the increase in traffic intensity and the increase in the number of drivers, there is an increase in the number of traffic jams which in turn leads to additional driver fatigue and, in some cases, to sleep.There is a need to take some measures to prevent traffic accidents due to fatigue and sleep. When studying such modern systems, it becomes clear that they work by analyzing the actions of the driver and the course of the car. The aim of the work is to conduct a comparative analysis of methods for determining driver fatigue and developing a new additional method. The paper shows that the main disadvantages of the systems under study are: complex design; adaptation to the driver; fatigue settling time and high system cost. It is proposed to introduce a pulse fatigue detection system (PFDS), which, by analyzing the number of pulse beats, will predict human fatigue and possible sleep, which will lead to a simpler design in systems with control of physiological parameters, that is-to reduce the price.

Entities ensuring road traffic safety and operation of road transport

Entities ensuring road traffic safety and operation of road transport

Application of Machine Learning Methods on Climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition

Accurately measuring meteorological visibility is an important factor in road, sea, rail, and air transportation safety, especially under visibility-reducing weather events. This paper deals with the application of Machine Learning methods to estimate meteorological visibility in dusty conditions, from the power levels of commercial microwave links and weather data including temperature, dew point, wind speed, wind direction, and atmospheric pressure. Three well-known Machine Learning methods are investigated: Decision Trees, Random Forest, and Support Vector Machines. The correlation coefficient and the mean square error, between the visibility distances estimated by Machine Learning methods and those provided by Burkina Faso weather services are computed. Except for the SVM method, all the other methods give a correlation coefficient greater than 0.90. The Random Forest method presents the best result both in terms of correlation coefficient (0.97) and means square error (0.60). For this last method, the best variables that explain the model are selected by evaluating the weight of each variable in the model. The best performance is obtained by considering the attenuation of the microwave signal and the dew point.

Focus on Tank truck drivers Vision in ensuring Safe Road transportation of hazardous petroleum products

Focus on Tank truck drivers Vision in ensuring Safe Road transportation of hazardous petroleum products

The Analysis of the Factors Influencing the Severity of Bicyclist Injury in Bicyclist-Vehicle Crashes

Transportation and technological development have for centuries strongly influenced the shaping of urbanized areas. On one hand, it undoubtedly brings many benefits to their residents. However, also has a negative impact on urban areas and their surroundings. Many transportation and technological solutions lead, for example, to increased levels of pollution, noise, excessive energy use, as well as to traffic accidents in cities. So, it is important to safe urban development and sustainability in all city aspects as well as in the area of road transport safety. Due to the long-term policy of sustainable transport development, cycling is promoted, which contributes to the increase in the number of this group of users of the transport network in road traffic for short-distance transport. On the one hand, cycling has a positive effect on bicyclists’ health and environmental conditions, however, a big problem is an increase in the number of serious injuries and fatalities among bicyclists involved in road incidents with motor vehicles. This study aims to identify factors that influence the occurrence and severity of bicyclist injury in bicyclist-vehicle crashes. It has been observed that the factors increasing the risk of serious injuries and deaths of bicyclists are: vehicle driver gender and age, driving under the influence of alcohol, exceeding the speed limit by the vehicle driver, bicyclist age, cycling under the influence of alcohol, speed of the bicyclist before the incident, vehicle type (truck), incident place (road), time of the day, incident type. The obtained results can be used for activities aimed at improving the bicyclists’ safety level in road traffic in the area of analysis.

Criminological legislation of Ukraine in the field of road safety

The level of road safety in Ukraine cannot be estimated as high. According to the reports of the Patrol Police, in Ukraine in recent years, on average, 3.5 thousand people die from road accidents every year.
 An increase in the level of road transport safety determines the development of a theoretical basis for legislation to ensure the prevention of offenses, primarily criminal ones. The criminological legislation in the field of road safety can be such a theoretical basis.
 The legislation, the norms of which are aimed at ensuring road safety, is significantly branchy and widely held in norms of laws of different legal force, forming, on the one hand, a large layer of legal norms, and on the other, laying the foundations for the prevention of offenses in this area.
 Criminological legislation in the field of road safety is a separate structural element of the criminological legislation of Ukraine. The subject of legal regulation in this case is public relations in the field of regulation of the prophylaxis and prevention of offenses (both administrative and criminal) in the provision of road traffic.
 According to their functional purpose, the norms of the criminological legislation of Ukraine in the field of road safety can be divided into conceptual, programmatic, regulatory and preventive ones.
 Conceptual norms include the norms through which the system of views and constructive ideas of the state about the road safety improving is revealed. The program norms of criminological legislation in the field of road safety include the provisions of documents aimed at the implementation of conceptual norms. Regulatory norms are the norms of legislation aimed at observing the rules, norms and standards for the operation of transport, ensuring the rules for organizing road safety and other prescriptions that determine the rules for road users. Preventive provisions include the norms of administrative and criminal legislation that determine responsibility for violations in the field of road safety and transport operation, thereby performing prophylactic and preventive functions.

Entities of international-legal provision of traffic safety

The article attempts to study the system of entities involved in the formation of the international-legal framework in the field of strengthening the traffic safety. In general, the relevance of the studied issues is outlined. Circumstances that indicate the need for an urgent solution to this acute social problem, which leads to the death and injury of millions of people every year in many countries were determined. The methodology of knowledge of participants of the international-legal maintenance of traffic safety and transport operation which includes a number of interrelated scientific parameters is offered. These are: the essential features of such subjects; their concepts; types; features of activity. The essential features of the considered subjects include: their legal status; form of activity; aim of activity. The separate criteria on which it is possible to carry out grouping of subjects in the considered sphere are specified. The position is defended that the most optimal basis for subjects classification of the international legal-provision of road traffic is their specialization. The basic subjects (specialized) and secondary subjects (non-specialized) in the specified sphere are distinguished. Particular attention is paid to specialized entities with international status: the United Nations, the World Health Organization, the World Bank and others. The multidirectional work of the UN as the main international institution, which is involved in the development and adoption of relevant international legal documents in the field under study, is analyzed in detail. The directions of activity of non-specialized entities, that participate in drawing the attention of the world community to the international legal regulation of certain aspects of road transport safety are identified