Risk Of Traffic Accidents Research Articles

ANALYSIS OF FACTORS AFFECTING DRIVING IN THE DARK TIME OF DAY

An analysis of factors affecting road traffic safety from the point of view of driving a car in the dark was carried out, in particular, the technical condition of the car, tinting of the windows, lighting of the streets (roads), speed of movement, driver fatigue. It has been established that a significant number of factors require significantly greater concentration and esponsibility from drivers. Driving a car in the dark requires special knowledge, skills and attention. The technical condition of vehicles becomes a number of priority problems. Vehicle malfunctions that are not detected in time or not eliminated increase the likelihood of a Road Traffic Accident. The cause of a Road Traffic Accident, especially in the dark period of the day, may be insufficient visibility of the road situation by the driver, which is caused by the violation of the requirements for tinting the glass in the car. The driver will not be able to fully assess everything that happens on the road at night both in conditions of insufficient visibility and in the presence of bad weather conditions. At the same time, proper street lighting can help reduce the risk of road traffic accidents by improving pedestrian visibility, reducing the likelihood of accidents and providing safer road conditions for both drivers and pedestrians. Speeding and incorrect selection of the speed regime in specific traffic conditions is one of the main factors affecting both the number and severity of Road Traffic Accidents. Another reason for the occurrence of a Road Transport Accident may be the driver’s fatigue, which reduces his concentration and ability to drive safely.

IMPROVING THE EFFICIENCY OF TRAFFIC LIGHT CONTROL AT ROAD INTERSECTIONS

From the functional point of view, the intersection is the most complex element of the road network. It is here that the traffic flows in different directions cross, and various maneuvers take place. This indicates that the intersection is a place with an increased concentration of conflict situations and an increased risk of traffic accidents. At most high-flow intersections, traffic is controlled by traffic lights, and their inefficient operation can lead to unnecessarily long wait times and overall increase in traffic delays. The research analyzed a regulated intersection at the crossing of Stepana Bandery and Viacheslava Chornovola main streets in the city of Rivne. This intersection is characterized by constant significant traffic jams. To simulate traffic conditions at this intersection, the research used PTV Vissim software. Observations of traffic flows, their distribution by directions, and traffic light regulation parameters were used as initial data for modeling the intersection traffic scheme. The main problem that must be solved when considering isolated traffic light intersections is the calculation and optimization of the signal plan, which involves: determining the number of phases; determining cycle duration; distribution, i.e. determining parts of available green time for each phase; modeling of traffic situations that may arise due to the passage of priority vehicles, congestion of vehicles in intersection areas during peak periods or other situations. At the same time, it is necessary to achieve the best possible characteristics of the intersection functioning. According to the research results, the road conditions were modeled and the signal plan was optimized with the use of PTV Vissim software. Simulation modeling of the intersection included drawing a road network, installing traffic lights (signal controllers) with a description of their work (choosing the type of light signaling devices, creating signal groups and traffic light signals, parameters for coordinating signals), forming pedestrian zones and a node, performing calculations with subsequent analysis of the received data. In order to improve the efficiency of the intersection, two options for the operation of the traffic light controllers are offered: 1. Lengthening of the “green” phase, for the convenience of turning to the left (by reducing the phase of oncoming traffic in one direction by 5 seconds). The total duration of the cycle of 70 seconds will not change; 2. Implementation of the third phase – fully pedestrian in all directions, lasting 20 seconds. The total duration of the cycle will increase to 90 seconds. A more progressive measure can be the introduction of adaptive systems, which are based on new traffic monitoring technologies and allow obtaining accurate data on traffic flows in real time and performing adaptive control of traffic lights, that is, adapting the signal plan in real time to changes in traffic flows.

Discretionary Lane-Change Decision and Control via Parameterized Soft Actor–Critic for Hybrid Action Space

This study focuses on a crucial task in the field of autonomous driving, autonomous lane change. Autonomous lane change plays a pivotal role in improving traffic flow, alleviating driver burden, and reducing the risk of traffic accidents. However, due to the complexity and uncertainty of lane-change scenarios, the functionality of autonomous lane change still faces challenges. In this research, we conducted autonomous lane-change simulations using both deep reinforcement learning (DRL) and model predictive control (MPC). Specifically, we used the parameterized soft actor–critic (PASAC) algorithm to train a DRL-based lane-change strategy to output both discrete lane-change decisions and continuous longitudinal vehicle acceleration. We also used MPC for lane selection based on the smallest predictive car-following costs for the different lanes. For the first time, we compared the performance of DRL and MPC in the context of lane-change decisions. The simulation results indicated that, under the same reward/cost function and traffic flow, both MPC and PASAC achieved a collision rate of 0%. PASAC demonstrated a comparable performance to MPC in terms of average rewards/costs and vehicle speeds.

Self-management of Risk for the Prevention of Traffic Accidents from a Health Perspective: A Qualitative Study.

Introduction: In the world, deaths and injuries caused by traffic collisions have been considered a public health problem. In Colombia, 7.238 fatalities were recorded in 2021, with motorcycle riders representing the largest group of victims at 59.7%. Methods: The aim of this qualitative phenomenological study is to describe the risky experiences and deliberate actions of diverse road users that influence the self-management of the risk of traffic collisions. Results: Data were obtained from 22 participants: motorists, pedestrians and drivers. The content analysis describes various human conditions that affect self-management of the risk of traffic accidents, such as unsafe behaviors, non-compliance with traffic regulations by the different road actors, competitive culture among drivers, eagerness, among others. Additionally, factors related to care were determined: healthy recreational activities, promoting the value of one's own life and that of others, adequate time management and preventive behaviors by some road users. Conclusion: This research provides information on social and cultural aspects, experiences and risky behaviors of different road actors that influence the incidence of traffic accidents in Colombia.

Design of a tracking controller for autonomous articulated heavy vehicles using a nonlinear model predictive control technique

This article proposes a design of a tracking controller for autonomous articulated heavy vehicles (AAHVs) using a nonlinear model predictive control (NLMPC) technique. Despite economic and environmental benefits in freight transportation, articulated heavy vehicles (AHVs) exhibit poor directional performance due to their large sizes, multi-unit vehicle configurations, and high centers of gravity (CGs). AHVs represent a 7.5 times higher risk of traffic accidents than single-unit vehicles (e.g. rigid trucks, cars, etc.) in highway operations. Human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous driving control of AHVs. To increase the safety of AHVs, we design a novel NLMPC-based tracking controller for an AHV, that is, a tractor/semi-trailer combination, and this tracking controller is distinguished from others with the feature of controlling both the lateral and longitudinal motions for both the leading and trailing units. To design the tracking controller, a new prediction AHV model is developed, which represents both the lateral and longitudinal dynamics of the vehicle and captures its rearward amplification feature over high-speed evasive maneuvers. With the proposed tracking controller, the AAHV tracks the predefined reference path and follows a planned forward-speed scheme. Co-simulation demonstrates the effectiveness and robustness of the proposed NLMPC tracking controller.

Road traffic accidents, sanctions and delays in judgments in Court proceedings in Prizren - Republic of Kosovo

Abstract Traffic accidents are unexpected and unpleasant cases on the road, which often have consequences of damage to cars, people’s injuries and in more severe cases lead to death. These events occur for various reasons and can be the result of factors such as high speed, disregard for traffic signals, alcohol, unused safety belt, technical vehicle problems, bad weather conditions and dangerous drivers’ behavior. The consequences of traffic accidents can be very serious, including serious physical injuries, material damage to the vehicle, and in some cases loss of life. Some of the preventive measures to reduce the risk of traffic accidents include the respect for traffic rules, use of seat belts, non-consumption of alcohol at the time of driving, car maintenance, education and information. Road safety is a common challenge that requires cooperation and dedication from drivers, road authorities and the community in general. This challenge is faced by law enforcement institutions to perform the tasks and obligations deriving from the legislation in terms of discouraging legal violations by traffic participants. Therefore, this paper is intended for the discovery and interpretation of any stagnation or negligence that can lead to the encouragement of traffic participants to neglect traffic rules and laws. The analysis of this manuscript is accompanied with statistics, tables and graphs from 116 cases from a population of about 150,000 inhabitants.

High magnitude of road traffic accident among traumatized patients in Addis Ababa, Ethiopia: A facility-based cross-sectional study

Background: Globally, road traffic accidents cause around 50 million injuries and disabilities and close to 1.35 million deaths every year. In Ethiopia, approximately five thousand deaths are estimated to occur annually due to road traffic accidents. However, there is limited data on the precise burden of the problem in Ethiopia. This study aimed to assess the magnitude of road traffic accidents and associated factors among traumatized patients who visited the emergency units of selected public hospitals in Addis Ababa, Ethiopia. Methods: A facility-based cross-sectional study design was conducted on 381 trauma patients who visited the emergency department of three public hospitals in Addis Ababa between June 1 and July 12, 2022. The data were collected using a structured questionnaire, entered into Epi-data, and exported to SPSS for statistical analysis. A logistic regression model was used to identify factors contributing to road traffic accidents at a significant level of P < 0.05. Results: The majority of participants were males (73.5%), between 20 and 39 years old (65.1%), and married (45.7%). The prevalence of road traffic accidents among traumatized patients was 37.3% (95% CI: 32.5, 42.3) in public hospitals in Addis Ababa. After adjusting for potential confounders, being male [AOR (95% CI) = 2.77 (1.6, 4.8)], traveling in the afternoon [AOR (95% CI) = 0.6 (0.3, 0.9)], and alcohol-drinking before travel [AOR (95% CI) = 2.50 (1.6, 4.0)] were factors contributing to road traffic accidents. Conclusion: The prevalence of road traffic accidents was very high in Addis Ababa public hospitals. Being male, traveling in the afternoon, and drinking alcohol before travel were significantly associated with an increased risk of road traffic accidents. Community awareness creation, strict traffic flow control in the morning, and alcohol breath tests will ultimately decrease road traffic accidents. In the meantime, expansion and strengthening of pre-hospital care and post-crash trauma care services.

Design and Development of an IoT System Prototype for Safe Traffic Assurance System

In 18 months (from 2022 to the first 6 months of 2023), there were 16,229 road traffic accidents nationwide, killing 9,086 people and injuring 11,235 people. In the first 9 months of 2023 alone, there were 8,335 traffic accidents nationwide, killing 4,765 people and injuring 5,802 people. Traffic order and safety are one of the important issues related to the life and development of the country. According to statistics from the World Health Organization (WHO), each year about 1.35 million people die and 50 million people are injured due to traffic accidents. Some serious traffic accidents, killing and injuring many people, are mainly caused by high speed and violation of the regulations on alcohol concentration. Therefore, implementing safe traffic solutions to minimize loss of life and property is one of the urgent needs. The project develops an IoT system prototype that can reduce the risks of traffic accidents through a continuous monitoring of parameters during participation in traffic by vehicles. The system integrates sensors, including gas sensors (detection of alcohol concentration), vibration sensors (detection of accidents), and infrared sensors (detection of the seat belts). In case of any accident, the system can send warnings and location information to the relevant systems.

Evaluasi Dampak Kegiatan Sekolah Terhadap Pola Lalu Lintas Dan Mobilitas Kota

This research aims to evaluate the impact of school activities on traffic patterns and city mobility by conducting a case study on a secondary school in an urban area. As a case study, the Jalan Tempeh section of Lumajang Regency, which contains SMA Negeri 1 Tempeh Lumajang. Research was conducted to understand how daily school activities, including start and leave times, school events, and extracurricular activities, influence traffic patterns and mobility around the school area. Research methods include field surveys to observe traffic patterns, calculating traffic volume during rush hours, interviews with school officials, and analysis of traffic accident data around the school location. The use of this combination of methods allows us to gain a comprehensive understanding of the impact of school activities on urban traffic and mobility. The research results show that school activities have a significant impact on traffic patterns around the school area, especially during rush hours. Traffic density increases significantly during school entry and exit hours, which has the potential to cause congestion and increase the risk of traffic accidents.

Analisis Dampak Kurangnya Fasilitas Service Road terhadap Pergerakan Penumpang di Apron Bandar Udara Internasional Husein Sastranegara Bandung

Service Road markings are very important and mandatory markings for every Domestic and International Airport which have an important role in supporting the safety of passenger movements and regular flight at Husein Sastranegara International Airport Bandung. The aim of this research isTo find out what causes the absence inApron Husein Sastranegara International Airport Bandung, to find out what the impact of its absence isService Road on the movement of passengers inApron Bandung Husein Sastranegara International Airport, and to find out the efforts of the AMC unit against their absenceService Road OfApron Bandung Husein Sastranegara International Airport. In this study, the research used qualitative methods with data collection techniques used were observation, interviews, and documentation. The research was carried out from 1 August 2022 to 30 September 2022 and continued with observation activities for 4 days from 24 February 2023 to 27 February 2023. Observations made in this study were observations and reviews at the Apron Movement Control Unit at Husein International Airport Bandaung Literature. The informants or respondents in this study were workers at the Apron Movement Control Unit at Husein Sasatranegara International Airport, Bandung. Documentation in this study is documentation of activities that occur in the Apron area or the movement of aircraft, passengers and workers. The results of the study show that Bandung Husein Sastranegara International Airport does not have a Service Road due to limited land which of course this can cause an impact or risk of aviation traffic accidents at Bandung Husein Sastranegara International Airport. Efforts made by the AMC Unit to overcome these impacts are by increasing the performance of the AMC Unit at Bandung's Husein Sastranegara International Airport.
 Keywords: Passenger Movement, Service Road, Bandung Husein Sastranegara International Airport.

Risk of traffic accidents in patients with sleep disorders

Risk of traffic accidents in patients with sleep disorders

Mining the Micro-Trajectory of Two-Wheeled Non-Motorized Vehicles Based on the Improved YOLOx

Two-wheeled non-motorized vehicles (TNVs) have become the primary mode of transportation for short-distance travel among residents in many underdeveloped cities in China due to their convenience and low cost. However, this trend also brings corresponding risks of traffic accidents. Therefore, it is necessary to analyze the driving behavior characteristics of TNVs through their trajectory data in order to provide guidance for traffic safety. Nevertheless, the compact size, agile steering, and high maneuverability of these TNVs pose substantial challenges in acquiring high-precision trajectories. These characteristics complicate the tracking and analysis processes essential for understanding their movement patterns. To tackle this challenge, we propose an enhanced You Only Look Once Version X (YOLOx) model, which incorporates a median pooling-Convolutional Block Attention Mechanism (M-CBAM). This model is specifically designed for the detection of TNVs, and aims to improve accuracy and efficiency in trajectory tracking. Furthermore, based on this enhanced YOLOx model, we have developed a micro-trajectory data mining framework specifically for TNVs. Initially, the paper establishes an aerial dataset dedicated to the detection of TNVs, which then serves as a foundational resource for training the detection model. Subsequently, an augmentation of the Convolutional Block Attention Mechanism (CBAM) is introduced, integrating median pooling to amplify the model’s feature extraction capabilities. Subsequently, additional detection heads are integrated into the YOLOx model to elevate the detection rate of small-scale targets, particularly focusing on TNVs. Concurrently, the Deep Sort algorithm is utilized for the precise tracking of vehicle targets. The process culminates with the reconstruction of trajectories, which is achieved through a combination of video stabilization, coordinate mapping, and filtering denoising techniques. The experimental results derived from our self-constructed dataset reveal that the enhanced YOLOx model demonstrates superior detection performance in comparison to other analogous methods. The comprehensive framework accomplishes an average trajectory recall rate of 85% across three test videos. This significant achievement provides a reliable method for data acquisition, which is essential for investigating the micro-level operational mechanisms of TNVs. The results of this study can further contribute to the understanding and improvement of traffic safety on mixed-use roads.

FDAN: Fuzzy deep attention networks for driver behavior recognition

Driver behavior is an essential factor affecting traffic safety, and driver behavior monitoring systems (DMSs) are widely exploited in intelligent transportation systems to reduce the risk of traffic accidents. However, understanding driver behavior is challenging because of the uncertainty of real driving scenarios. Most of the existing methods use deterministic models, which suffer from data uncertainty, for recognizing driver behaviors. In this paper, the fuzzy deep attention network (FDAN) method is proposed to improve driver behavior recognition. FDAN integrates fuzzy logic and an attention mechanism into deep neural networks, which enhances the representation ability of the model and reduces the uncertainty of the data. The attention mechanism with a lightweight squeeze-and-excitation block is embedded in the deep learning model for adaptively refining features. A DMS is designed, and the distracted driver dataset from the real scene is built. Experimental results confirm the proposed method performs better than the existing methods.

Rationalization of Selected Crossing Security Devices on Czech Railways Line 201 Tábor – Ražice

The paper focuses on improving the safety of selected railway crossings on the Tábor – Ražice line of the Czech Railways. The main goal is to rationalize existing safety measures and propose alternative modifications to increase the safety of crossings. These include, for example, the use of horizontal markings and the use of lockable barriers. The work also deals with the elimination of the possibility of some crossings with the aim of minimizing the risks of traffic accidents and thus improving overall safety on the track. The results provide practical recommendations for increasing crossing safety.

Road Traffic Accident Risk Prediction and Key Factor Identification Framework Based on Explainable Deep Learning

Road Traffic Accident Risk Prediction and Key Factor Identification Framework Based on Explainable Deep Learning

Predicting Traffic Accident Risk in Seoul Metropolitan City: A Dataset Construction Approach

Predicting Traffic Accident Risk in Seoul Metropolitan City: A Dataset Construction Approach

DualRisk: A Two-Branch Model for Sparse Traffic Accident Risk Forecasting

DualRisk: A Two-Branch Model for Sparse Traffic Accident Risk Forecasting

Traffic Accident Risk Prediction of Tunnel Based on Multi-Source Heterogeneous Data Fusion

Traffic Accident Risk Prediction of Tunnel Based on Multi-Source Heterogeneous Data Fusion

Analysis of the Effectiveness of Motor Vehicle Insurance at PT. Jasa Raharja

This study aims to test the analysis of the effectiveness of motor vehicle insurance at PT. Jasa Raharja branch Medan. Insurance at PT. Jasa Raharja Motor are an effort to protect citizens, especially from the risk of road traffic accidents, to provide compensation to victims of traffic accidents or to their heirs. This research took 4 respondents, consisting of 1 employee PT. Jasa Raharja, and 3 people who were victims of traffic accidents, and received compensation funds. This method uses interview techniques. The interview technique was carried out in an unstructured manner using an interview guide. Research data analysis is descriptive. The results of this research indicate that PT. Jasa Raharja still seems to be good and consistent in carrying out its duties as a fund collector and distributor of traffic accident compensation funds. Traffic accident victims and recipients of compensation funds revealed that PT. Jasa Raharja is satisfied with the services provided. The benefits obtained at PT. Jasa Raharja are indeed effective and efficient because they have achieved the targets desired by PT. Jasa Raharja Branch Medan.

Research on Traffic Accident Risk Prediction Method Based on Spatial and Visual Semantics

Predicting traffic accidents involves analyzing historical data, determining the relevant factors affecting the occurrence of traffic accidents, and predicting the likelihood of future traffic accidents. Most of the previous studies used statistical methods or single deep learning network model prediction methods while ignoring the visual effects of the city landscape on the drivers and the zero-inflation problem, resulting in poor prediction performance. Therefore, this paper constructs a city traffic accident risk prediction model that incorporates spatial and visual effects on drivers. The improved STGCN model is used in the model, a CNN and GRU replace the origin space–time convolution layer, two layers of a GCN are added to extract the city landscape similarity of different regions, and a BN layer is added to solve the gradient explosion problem. Finally, the features extracted from the time–space correlation module, the city landscape similarity module and the spatial correlation module are fused. The model is trained with the self-made Chicago dataset and compared with the existing network model. The comparison experiment proves that the prediction effect of the model in both the full time period and the high-frequency time period is better than that of the existing model. The ablation experiment proves that the city landscape similarity module added in this paper performs well in the high-frequency area.