Accident Black Spots Research Articles

Analyzing road traffic accidents through identification and prioritization of accident-prone areas on the dembecha to injibara highway segment in amhara region, ethiopia

Every year, millions die in road accidents globally, imposing significant economic and humanitarian costs. While road traffic accidents are a major health concern, many developing countries, including Ethiopia, struggle to address this issue effectively. Ethiopia ranks second in East Africa for severe road traffic accidents, highlighting the need for improved injury reduction strategies. This study introduces a novel approach by chronologically identifying and prioritizing accident black spots in the studied area, Ethiopia. This method provides a valuable tool for transportation authorities and traffic police to target high-risk areas for immediate intervention. Focusing on the Dembecha-Injibara highway segment, the study employs both descriptive and inferential analyses, using the Zegeer method to calculate accident rates. It also uses factors of weight contributing to road traffic accidents and their severity to rank accident-prone areas. The findings reveal that areas near Finote Selam, Banja, and Burie are highly prone to severe accidents, with specific accident frequencies and priority values identified. Recommendations are offered to address these high-risk areas and mitigate severe traffic accidents in the study region.

Analysis of the Relationship between Speed Consistency and Traffic Accident Probability (on the Mojokerto-Surabaya Toll Road)

Vehicle speed consistency plays a crucial role in maintaining traffic safety, with speed limits set to reduce accident fatalities. While the direct impact of speed limits on vehicle speed is minimal, their implementation significantly influences driver behavior, with about 85% of drivers complying with speed limits. However, variables such as traffic volume, weather conditions, and travel urgency often cause drivers to be inconsistent in adhering to these limits, increasing accident risk. On the Mojokerto-Surabaya toll road, data from IRSMS Korlantas Polri and Jasa Marga recorded 193 accidents with 101 fatalities from 2018 to August 2023, mostly caused by driver behavior such as excessive speed. This study analyzes the relationship between speed consistency and accident probability on this road, highlighting accident issues due to speed violations and inconsistency at ramp off/on locations. Results show the importance of toll road infrastructure considering vehicle volume, identifying accident blackspots, and implementing urgent speed management as key factors in improving safety on the Surabaya-Mojokerto toll road. The findings emphasize the need for better traffic management and speed regulation to reduce fatal accident risks.

Accident black spots identification based on association rule mining

This paper presents an analytical approach to identifying the important characteristics of accident black spots on Moroccan rural roads. An association rule mining method is applied to extract road spatial characteristics associated with fatal accidents. The weighted severity index was calculated for each section, which was then used to determine the severity levels of black spots. The apriori algorithm is applied to find the correlation between road characteristics and the severity levels of black spots. Then, a general rule selection method is proposed to identify the rules strongly associated with each severity level. The results show that the proposed approach is effective in identifying the most important factors contributing to accidents. Furthermore, it shows that the combination of several road characteristics, such as road width, road surface, and bridge presence, may contribute to fatal accidents. The general rule selection found that wet, bad surfaces, and narrow shoulders were significantly associated with accidents on rural roads. The findings of the present study can help develop effective strategies to reduce road accidents and thus improve road safety in the country.

Accident black spot clustering oriented maritime search and rescue resource allocation and optimization

Efficient and rapid deployment of maritime search and rescue(MSAR) resources is a prerequisite for maritime emergency search and rescue, in order to improve the efficiency and accuracy of MSAR. This paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents.

Review Paper on Road Safety Analysis

Road safety analysis refers to thoroughly examining roads and general transportation infrastructure using various analytical methods. It usually involves a multidisciplinary team with the local governing body and other stakeholders to check for potential safety issues and bring improvements within the same. The purpose of this review paper is to study the causes of road accidents and identify the active blackspots present in Raipur city. The goal is to prevent accidents by making sure road infrastructure is designed and optimised for safety and efficiency. According to the Ministry of Road Transport & Highways (MoRTH), Government of India, road accident blackspot on National Highways is a road stretch of about 500m in length in which either 5 road accidents (involving fatalities/grievous injuries) took place during the last three calendar years or 10 fatalities took place during the last three calendar years. Through detailed study and analysis, one can surmise that Road Safety Analysis can be easily broken down to factors such as types of road users, presence of traffic calming devices, weather conditions, whether users possess a driver’s licence and much more. Furthermore, it is important to address the deficiency and failure of one or more elements in road infrastructures to curb the issue of road accidents by understanding the data obtained accordingly. Key Word: Road accidents, Blackspots, Road Safety Analysis

IDENTIFICATION OF ACCIDENT LOCATION AND DEGREE OF VULNERABILITY ALONG OSOGBO-GBONGAN ROAD, OSUN STATE, NIGERIA

Road traffic accidents (RTAs) remain a significant global concern and leading cause of death of people within the economically active age-group in developing countries thereby mounting negative effect on people’s health, economy and the society at large. This research aimed at developing a prediction model for accident prone areas along Osogbo-Gbongan road as past studies revealed that the study area reported the highest RTAs in Osun State, Nigeria. Four years accident data obtained from FRSC, Osogbo Branch was characterized in terms of casualty, accident causes and location and affected age-groups (Adult/Children). Geometric parameters were obtained onsite by physical measurement to estimate the vulnerability of the road at blackspot locations. The identified accident blackspots were Ogo-oluwa (02+000), Ataoja (04+000), Abeere (06+000), Owode (10+000) and Akoda (15+000) with their vulnerability indices as 53, 67, 78, 66 and 70, respectively. The obtained total occurrence of accidents in 2018, 2019, 2020 and 2021 were 144, 128, 168 and 313, respectively with speed violation as the highest causative factor. Abeere Area has the highest accident occurrence of 31 and the highest T.SPWEG-A.V.I of 78 with three other locations showing similar pattern except Owode Area. Therefore, this model showed a perfect correlation of 80% thereby ensuring the validity of the model for predicting the accident vulnerability at accident locations. This study concluded that accident in this area can be characterized as minor, serious and fatal with the fatal causing deaths. The speed violation and poor road geometry were the primary factors contributing to the severity of accident and it will continue to increase if urgent intervention is not applied. The study recommended that speed law should be enforced, presence of safety personnel at the noon part of the day and those in charge of road design, supervision and construction should uphold good practice and rehabilitate the existing road geometry so as to curtail accident reoccurrence along the study area.

Identification and analysis of accident black spots using Geographic Information System (GIS): A study on Kushtia-Jhenaidah national highway (N704), Bangladesh

Road accidents, mostly on national highways, pose a significant public health and economic burden in Bangladesh, requiring in-depth analysis for road safety measures. This study comprehensively analyzes accident trends, characteristics, causes, and consequences by identifying the accident black spots on the Kushtia-Jhenaidah National Highway (N704). Accident records from 2017 to 2021 were collected from nearby police stations. Additionally, using a cluster random sampling approach, a questionnaire survey with 100 respondents (50% drivers and 50% general road users) was also conducted to capture diverse perceptions and behaviors. The study utilizes descriptive methods, such as trends analysis and frequency distributions, alongside spatial analysis techniques, including severity index, Kernel Density Estimation, and hotspot analysis. Findings indicate a decrease in accidents from 2018 to 2021, yet a concerning rise in fatalities in 2021. Trucks (47.9%) emerge as the primary contributor among 169 vehicles involved in accidents. Head-on collisions (36%) are prevalent, attributed to both human and environmental factors, including driver inexperience (56%), mobile phone use while driving (78%), lack of proper training (12%), overspeeding (28.3%), and nighttime driving (54%) influenced by seasons and land use. Mostly, victims aged from 20 to 40, where men are more affected by fatalities (70.7%) and women by injuries (86.3%). Out of 35 identified accident spots, including Battail, Bittipara Bazar, Laxmipur Bazar, Modhupur Bazar, IU Main Gate, Sheikhpara Bazar, and DM College Front, are designated as blackspot zones based on the frequency of accidents, deaths, and injuries. The study concludes by recommending targeted interventions, driver training, infrastructure improvements, regulatory measures, and victim assistance in collaboration with local and national agencies to enhance road safety and mitigate accident risks.

A Multi-Objective Optimization Method for Maritime Search and Rescue Resource Allocation: An Application to the South China Sea

To effectively address the increase in maritime accidents and the challenges posed by the trend toward larger ships for maritime safety, it is crucial to rationally allocate the limited maritime search and rescue (MSAR) resources and enhance accident response capabilities. We present a comprehensive method for allocating MSAR resources, aiming to improve the overall efficiency of MSAR operations. First, we use long short-term memory to predict the number of future accidents and employ the K-medoids algorithm to identify the accident black spots in the studied area. Next, we analyze the multi-constraint conditions in the MSAR resource allocation process. A multi-objective integer programming model is constructed to minimize the response time and allocation cost. Finally, we use the non-dominated sorting genetic algorithm II (DNSGA-II) with Deb’s rules to solve the model, and we propose a multi-attribute decision optimization-based method for MSAR resource allocation. We found that the DNSGA-II exhibits better convergence and generates higher-quality solutions compared to the NSGA-II, particle swarm optimization (PSO), and enhanced particle swarm optimization (EPSO) algorithms. Compared with the existing MSAR resource emergency response system, the optimized scheme reduces the response time and allocation cost by 11.32% and 6.15%, respectively. The proposed method can offer decision makers new insights when formulating MSAR resource allocation plans.

V2I-Coop: Accurate Object Detection for Connected Automated Vehicles At Accident Black Spots With V2I Cross-Modality Cooperation

V2I-Coop: Accurate Object Detection for Connected Automated Vehicles At Accident Black Spots With V2I Cross-Modality Cooperation

Identification of Traffic Accident Black Spots on Suburban Highways Based on Smartphone Sensors of Drivers

Identification of Traffic Accident Black Spots on Suburban Highways Based on Smartphone Sensors of Drivers

Forgiving Road Design and Improvement of Accident Black Spots: A Case Study for Düzce City

The aim of changing the causes and consequences of traffic accidents, preventing possible human deaths, and providing more comfortable navigational safety is a common situation especially in the highway sections of our country where traffic accidents occur frequently. In the study, the concept of the forgiving road, which was brought to the agenda of our country by the General Directorate of Highways (KGM) in 2013 and still has problems in implementation, was examined. Investigations have been made on the highway sections where the same type of accidents occur frequently, and it has been emphasized how these highway sections can be forgiving roads. A correct and planned road design or improvement will directly contribute to a dramatic reduction in accident rates. In this context, three different accident points that need improvement within KGM have been determined. Considering the forgiving road design features on a total of three highway sections, on-site investigations were made, and recommendations were made.

Accident Analysis and Identification of Black Spots on Road Section from Sanga to Dhulikhel in Araniko Highway

Road accidents are major causes of injuries and fatalities worldwide and a significant public health concern. This paper includes a comprehensive analysis of road accidents on the road section from Sanga to Dhulikhel of Kavrepalanchwok district to identify trends of accidents, causes contributing to accidents and identifying the accident black spots and ranking accident black spots based on the Weighted Severity Index and Accident Severity Index. The study uses a data-driven approach using accident data obtained from the district police office, statistical analysis and weighted Severity Index and Accident Severity Index to observe road accident patterns. This study focuses on accident analysis to find out the spatial distribution of accidents, highlight accident peak times, accident black spots and their ranking. The various factors contributing to road accidents related to driver behaviour, vehicle condition, pedestrian behaviour, and environmental condition were explored and statistical analysis are done to determine accident likelihood and severity which are very helpful to identify accident-prone areas. There is a noteworthy pattern in the accident records in the past four fiscal years. Overall, fatal accidents have decreased over the years but serious accidents have increased. The number of minor incidents has also shown some variation. Males had more accidents than females. It is clear that the male percentage significantly outnumber the female comprising 81.9%, while females make up only 10.1%. It was observed that the 26 to 56 age group is a more vulnerable. The relatively low percentages of accidents occurred during morning and late evening. The accident frequency significantly rises from the late morning through the early afternoon with peak frequency in the late afternoon. It was observed that light vehicles account for 25%, tipper and other vehicles account for 19%, two wheeler accounts for 20% and vehicle and pedestrian accounts for 36% of the total accidents recorded. This study suggests evidence-based recommendations for accident prevention, infrastructure improvement, public awareness programs and law enforcement strategies to reduce road accidents.

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.

Assessing Road Accident Black Spots and Countermeasures in the State of Perak, Malaysia

To develop a successful strategy for improving road safety, the initial step involves is to identify the black spots that are more susceptible to high-risk traffic accidents. However, due to the high cost of implementing safety measures in these locations, only a limited number of spots/sites can be thoroughly examined with the available funding. Therefore, it is crucial to prioritize the high-risk sites and their corresponding safety measures in order to make the most efficient use of the available finances. Hence, this paper presents black spots ranking and low-cost countermeasures based on the Perak Federal trunk road traffic crash (RTC) data for three years from 2018 to 2020 recorded by the Royal Malaysian Police (RMP). The weightage point method was used for each black spot ranking exercise where as, the selection for countermeasures were based on RTC situations which are norms practiced by the Public Works Department (PWD). In total 22 black spots were ranked and it was found that the highest rank of the 2020 black spots in Perak was along route FT005 in Kinta district which had 18 RTCs and with a total weightage points (TWP) of 118.2. In balancing between limited financial allocation and improving road safety, the countermeasures were priorotized for implementation in 2021 based on the black spots ranking results. However, only six black spots were treated in 2021 with the entire cost was RM 1,834,027.19. Where for route FT005 in Kinta district, the countermeasure was done at Section 684 with a total cost of RM350,000 under the 2022 black spots countermeasure program which is an annual program funded by the Ministry of Works Malaysia. It is recommended that Perak State PWD form a Road Safety Unit to reduce the black spots along the state roads which is the second highest road fatalities recorded in Perak.

Driver drowsiness modeling based on spatial factors and electroencephalography using machine learning methods: A simulator study

Driver drowsiness is one of the leading causes of fatal road traffic accidents (RTA). While studies have illustrated the effectiveness of spatial criteria on driver drowsiness, the effects of these factors on drowsy driver behavior have yet to be mapped or spatially modeled properly. Meanwhile, driver behavior has been known as a major factor in the RTA occurrence. This paper investigated the effect of four spatial criteria of chainage, distance traveled after a horizontal curve, horizontal curvature, and slope on drowsy driver behavior in a simulated driving task. Accordingly, the drowsiness level on a monotone 108 km virtual expressway in northeastern Iran was determined by modeling the EEG data of 20 subjects. Four machine learning (ML) algorithms, namely bagged decision trees (BDT), multi-layer perceptron (MLP), random forest (RF), and support vector regression (SVR), were used to analyze electroencephalogram (EEG) signals and to estimate driver drowsiness based on spatial criteria. The results of the receiver operating characteristic curve (ROC) showed that BDT (0.925), RF (0.919), MLP (0.807), and SVR (0.706) had the highest accuracy in estimating drowsiness level on the virtual road using EEG signals, respectively. Using the estimated drowsiness level by BDT and spatial criteria values, ML algorithms were trained to map driver drowsiness on the Tehran-Mashhad road. Based on ROC results, MLP (0.921), BDT (0.918), RF (0.917), and SVR (0.896) had the most accurate performance in mapping driver drowsiness, respectively. Generated drowsiness level maps were validated, and the estimations of BDT and RF were more consistent with real drowsy driving accident black spots in the study area. In conclusion, mapping driver drowsiness by applying ML algorithms and spatial criteria detects high-risk areas and helps to improve road safety.

Black Spot Location Identification for National Highway: A Case Study of Mugling – Kotre Section of Prithivi Highway of Nepal

The rapid development of road infrastructure enhances connectivity and at the same time increases road crashes. A road traffic accident is one of the leading causes of injury and death in Nepal that has a great impact on the country’s economy. The identification of frequent road traffic crash spots and their causes are essential for the implementation of the most efficient countermeasures. Accident black spots are high-risk spots where numbers of accidents take place frequently and repeatedly. The study focused on the identification of black spots on the Mugling-Kotre section of the Prithivi highway of Nepal to provide knowledge for possible preventive measures for the improvement of road safety. The weighted severity index (WSI), prioritization index (PI), and focus group discussion have been performed to evaluate the safety status in the proposed area. The accident data were analyzed based on the weighted severity index (WSI), and the prioritization index (PI) was estimated by assigning a suitable weight to the factors contributing to an accident. Based on these results, six accident spots have been found critical regarding traffic safety. The result obtained from WSI and PI was further evaluated based on the focus group discussion so that essential safety measures could be implemented to enhance road safety.

Comparisons on methods for identifying accident black spots using vehicle kinetic parameters collected from road experiments

Comparisons on methods for identifying accident black spots using vehicle kinetic parameters collected from road experiments

Deep RNN Based Prediction of Driver’s Intended Movements at Intersection Using Cooperative Awareness Messages

This paper presents an early prediction framework to classify drivers’ intended intersection movements in a connected vehicle environment. Intersections are considered accident blackspots with major traffic violations that cause property damage, injuries and fatalities. An accurate perception of drivers’ intended movements at intersections is required for advanced red-light (ARLW) or turning warnings for vulnerable road users (TWVR). Early prediction of intersection movement and adequate warning assistance will ensure road users’ safety at the intersection. In this study, we adopted recurrent neural networks (RNN): long short-term memory (LSTM) and gated recurrent units (GRU) networks to predict driver intended movements at intersections using the vehicle kinematics extracted from the Cooperative Awareness Messages (CAMs). We used naturalistic driving data of the Ipswich Connected Vehicle Pilot (ICVP) project, Queensland, which was collected from 351 participants who drove their connected vehicles during the pilot period. The pilot study installed roadside equipment at 29 signalised intersections to enable the Cooperative Intelligent Transportation System (C-ITS) use cases. Vehicle speed, speed limit, longitudinal acceleration, lateral acceleration, and yaw rate are used as predictors and monitored in 100-millisecond intervals for 1s to 4s at different warning distances from the stop line. Separate prediction models are trained based on different monitoring windows. Furthermore, drivers’ intended intersection movements are predicted at two individual intersections to evaluate intersection-specific prediction performance and are found with improved prediction accuracy than overall prediction models trained with all 29 intersections data. Overall prediction models are useful for some intersections which lack available data for individual intersection-based prediction.

Black spots identification on rural roads based on extreme learning machine

<span lang="EN-US">Accident black spots are usually defined as road locations with a high risk of fatal accidents. A thorough analysis of these areas is essential to determine the real causes of mortality due to these accidents and can thus help anticipate the necessary decisions to be made to mitigate their effects. In this context, this study aims to develop a model for the identification, classification and analysis of black spots on roads in Morocco. These areas are first identified using extreme learning machine (ELM) algorithm, and then the infrastructure factors are analyzed by ordinal regression. The XGBoost model is adopted for weighted severity index (WSI) generation, which in turn generates the severity scores to be assigned to individual road segments. The latter are then classified into four classes by using a categorization approach (high, medium, low and safe). Finally, the bagging extreme learning machine is used to classify the severity of road segments according to infrastructures and environmental factors. Simulation results show that the proposed framework accurately and efficiently identified the black spots and outperformed the reputable competing models, especially in terms of accuracy 98.6%. In conclusion, the ordinal analysis revealed that pavement width, road curve type, shoulder width and position were the significant factors contributing to accidents on rural roads.</span>

Identifying Road Accident Black Spots using Classical and Modern Approaches

The utilization of conclusions from the data analysis of road traffic accidents is of high importance for the development of targeted traffic safety measures, which will effectively reduce the rate of road traffic accidents, thus promoting road safety. Considering the problems of time and money, it is not practical to improve road safety in all the places where road traffic accidents occur. Therefore, the process of identifying accident-prone locations, known as black spots, is a cost-effective and efficient way to analyze the causes of road accidents and reduce them. Identifying black spots is an effective strategy to reduce accidents. The core methods that may be used in the process of identifying the black spots of a road network are the sorting, grouping, and accident prediction methods. However, in practice, it is easy to overlook certain factors that significantly contribute to defining and characterizing a spot on the road network as black. Therefore, suggestions to carry out projects required to reduce security risks shall not be based on the above methods. Machine learning algorithms that in recent years have been widely used in the field of predicting a road traffic accident cover these weaknesses. They can effectively classify data sets and make a connection between factors and the severity of events. Machine learning algorithms include classification, regression, clustering, and dimensionality reduction. In this work, a study was conducted on road traffic accidents that took place on the national and provincial network of Northern Greece from 2014 to 2018, with the aim of determining the black spots. The study provided the general public access to a database of black spots on the road network of Northern Greece. At the same time, it created a point of reference for the recognition of the points in question located on the entire road network, and selected a black spot determination model, after having compared specific measures to determine the quality of a model, which resulted from the application of a logistic regression and machine learning algorithms.