Black Spot Identification Research Articles

Black-Spot Identification from Ramol Cross Road to Kathwada Cross Road

Road accidents are a vital issue that is occurring all over the world. Accident severity is increasing with the exponential increase in vehicle population. Accident leads to disability, pain, suffering, grievous injuries, hospitalization expenditure, damage to health and property, social suffering, general degradation of the environment, and, in extreme cases, death. This study aims to identify black spots in the selected stretch involving four major intersections from Ramol crossroad to Kathwada crossroad of Ahmedabad. Data collection consists of collecting past accident data from traffic police station headquarters. The collected data are categorized into fatal accidents, grievous significant accidents, and minor accidents. The data is analyzed using the accident data collected, the Weighted Severity Index (WSI) method, and the PTV Visum software. WSI is estimated for the accident data for the year 2019-2023. By accident analysis, seven black spots were identified in this study. Inspecting the road stretch noted several issues and probable causes of accidents were identified. Poor road conditions, including cracks and potholes, affect road safety. Improper road markings and signs are one of the causes of accidents. The audited road stretch includes recommendations to prevent the number and severity of accidents.

A Comprehensive Literature Review of Traditional and Spatial Methods for Black Spot Identification in Road Crash Analysis

The desire to greatly reduce traffic accidents, enhance road safety, and remove black spots from junctions has led to an increased interest in researching the various black spot (BS) identification systems. Conventional and spatial Black spot detection systems are created using a variety of concepts and methodologies; however, as each approach has a unique input and output based on zones, area, size, and other factors, it might not be suitable for every situation. The next step in this research is to conduct a comprehensive literature review of various Black spot detection approaches and the technology tools that support them. The goal is to identify, evaluate, and assess the acceptability, feasibility, accuracy, and appropriateness of various scenarios and parameters. Through accounting for several types of inadvertent correlation between the location of the accident and traffic information, the research discovered multiple methods for Black spot identification, each based on unique ideas and a range of methods and instruments. According to the study, each BS identification method has particular advantages and disadvantages. The principal objectives of this review are to assess the principal methods for evaluating traffic accidents, identify black spots, and explore possible distinctions between traditional and spatial traffic data analysis. To execute the statistical outcomes of occurrences that are examined, both the conventional method and GIS are used. This review paper provides an overview of three basic GIS approaches, explains a traditional method for identifying traffic incidents, and provides some often-used accident analysis tools for traffic safety.

Identification & Analysis of Black Spots on National Highways – A Current Scenario

Identification & Analysis of Black Spots on National Highways – A Current Scenario

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.

Proposed segment length for safety evaluation studies on rural divided highways in India

Determining an appropriate segment length for highway safety evaluations in low- and middle-income countries (LMICs) poses a significant challenge. This study aims to address this issue by recommending a suitable segment length for such evaluations in India, using a 167 km intercity expressway as a case study. We employed negative binomial (NB) models on datasets segmented from 100 m to 1000 m with 100 m increments. Our findings strongly suggest that segment lengths from 300 m to 700-m suit various safety assessments. However, the study reveals that parameter estimates vary significantly with both segment length and sample size. This highlights the sensitivity of parameters to data aggregation and sample size across different segment lengths, making it difficult to identify a single optimal length. Therefore, we propose selecting the segment length and segmentation approach based on specific local conditions, highway context, data availability and quality. The methodology presented here can guide policymakers in LMICs to make informed choices regarding segment length for safety evaluations, including blackspot identification and treatment on their highways.

Identification of Road Black Spots Based on the Sliding Window Optimization and Safety Performance Function Development

The sliding window method is a road network screening approach commonly used for identifying black spots. Previous studies have indicated that the selection of window length significantly impacts the black spot identification process. This research proposes a new method that optimizes the sliding window framework by examining its characteristics. The optimization methodology employed in this study is as follows: Firstly, the road is segmented, and for each segment, different scenarios of window lengths are chosen using the Density-Based Spatial Clustering of Applications with Noise algorithm. Next, a Safety Performance Function is developed to calculate the predicted and expected number of crashes, as well as the Potential Safety Improvement, for each window movement across all selected scenarios within the segment. Subsequently, the average differences are calculated using the analysis of variance, and the window length with the lowest dispersion of difference values from the mean is identified as the optimal length for each segment. The case study yielded noteworthy results, indicating that the utilization of the sliding window with optimal lengths led to the identification of 122 high-risk black spot-candidates. These points exhibit a higher crash density, effective length, and greater value in quantitative evaluation tests compared to the results obtained using windows with common fixed lengths.

An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks

Traditionally, research in the field of traffic safety has predominantly focused on two key areas—the identification of traffic black spots and the analysis of accident causation. However, such research heavily relies on historical accident records obtained from the traffic management department, which often suffer from missing or incomplete information. Moreover, these records typically offer limited insight into the various attributes associated with accidents, thereby posing challenges to comprehensive analyses. Furthermore, the collection and management of such data incur substantial costs. Consequently, there is a pressing need to explore how the features of the urban built environment can effectively facilitate the accurate identification and analysis of traffic black spots, enabling the formulation of effective management strategies to support urban development. In this study, we research the Kowloon Peninsula in Hong Kong, with a specific focus on road intersections as the fundamental unit of our analysis. We propose leveraging street view images as a valuable source of data, enabling us to depict the urban built environment comprehensively. Through the utilization of models such as random forest approaches, we conduct research on traffic black spot identification, attaining an impressive accuracy rate of 87%. To account for the impact of the built environment surrounding adjacent road intersections on traffic black spot identification outcomes, we adopt a node-based approach, treating road intersections as nodes and establishing spatial relationships between them as edges. The features characterizing the built environment at these road intersections serve as node attributes, facilitating the construction of a graph structure representation. By employing a graph-based convolutional neural network, we enhance the traffic black spot identification methodology, resulting in an improved accuracy rate of 90%. Furthermore, based on the distinctive attributes of the urban built environment, we analyze the underlying causes of traffic black spots. Our findings highlight the significant influence of buildings, sky conditions, green spaces, and billboards on the formation of traffic black spots. Remarkably, we observe a clear negative correlation between buildings, sky conditions, and green spaces, while billboards and human presence exhibit a distinct positive correlation.

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.

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.

Towards Sustainable Transportation: The Role of Black Spot Analysis in Improving Road Safety

Sustainable transportation goals include an improvement in the level of road safety worldwide. It is well known that traffic accidents are one of the major causes of death worldwide. Black spots are road locations with a higher than statistically expected number of accidents. Remedying black spots would decisively improve road safety. A literature review of black spot identification methods, i.e., accident numbers, accident rates related to exposure, severity of accidents, Poisson and quality control methods, is presented within the framework of this paper. The various approaches adopted by key European and other countries are also summarized and evaluated. An important parameter is the unit length of a road, where accidents are referred. The quality of accident records is also critical. It is concluded that the coupling of statistical and accident severity index methods can contribute to assessing road infrastructure in a more holistic way and, therefore, in providing more reliable results with regard to the road safety level. The design and implementation of effective road safety strategies, based on black spot analysis, can be of great value for the decision makers and decision takers who are involved in the development of a sustainable transportation system.

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.

Deep Learning-Based Black Spot Identification on Greek Road Networks

Black spot identification, a spatiotemporal phenomenon, involves analysing the geographical location and time-based occurrence of road accidents. Typically, this analysis examines specific locations on road networks during set time periods to pinpoint areas with a higher concentration of accidents, known as black spots. By evaluating these problem areas, researchers can uncover the underlying causes and reasons for increased collision rates, such as road design, traffic volume, driver behaviour, weather, and infrastructure. However, challenges in identifying black spots include limited data availability, data quality, and assessing contributing factors. Additionally, evolving road design, infrastructure, and vehicle safety technology can affect black spot analysis and determination. This study focused on traffic accidents in Greek road networks to recognize black spots, utilizing data from police and government-issued car crash reports. The study produced a publicly available dataset called Black Spots of North Greece (BSNG) and a highly accurate identification method.

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>

Black Spot Identification and Road Accident Prediction Model on Cebu South Coastal Road (CSCR)

Millions of people die in road accidents yearly, causing significant damage to the economy and humanity. The main focus of the study is to analyze road accidents through Black Spot Identification and to establish a Prediction Model on the Cebu South Coastal Road (CSCR)-N840 Highway. The data gathered from government agencies were digitized using SPSS and MS Excel. The study revealed that the year 2018 has the highest number of accident cases with 555 (46.52%) and the lowest severity ratio of 0.41. In contrast, 2019 has the lowest frequency of accident cases with 159 (13.33%) and a severity ratio of 0.72. Through the APW system, the CSCR-N840 Highway’s top black spot locations were at 2.6 km, 1.2 km, 2.3 km, 0.9 km,2.2 km, 5.3 km, 4.6 km, 4.7 km, 3.7 km, 5.9 km, 7.6 km, 7.7 km, 8.2 km, 8.5 km, 6.5 km, 10.2km, 11.4 km, 9.4 km, 12.1 km, and 12.0 km. Also, the researchers presented a heat mapping through QGIS for visual analysis, where severe road traffic accidents usually occur on the Talisay side of CSCR N840 Highway due to 13 junctions, 7 intersections, and 6 curvatures. Lastly, in the developed regression model the accident rate decreases gradually with an increase in the traffic volume. Also, Poisson prediction model, as the accident probability increases, the accident rate may either increase or slowly decrease.

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.

Spatial scale and spatial effects: Shedding light on how a change of scale can affect the characterization of environmental health inequalities

Resolving health inequalities continues to be a priority in improving quality of life around the world. Their impact is of increasing concern to public health governance, which considers the issue of environmental health inequalities to be paramount. To meet this challenge, we have developed a methodology based on the development of spatial composite index in order to assess these inequalities while also providing an alternative to classic approaches that are solely based on territory weaknesses. The aggregation of the widest possible range of factors involved in environmental health provide a clear picture of a territory. This study uses different cases to understand how the change of scale influences the analysis of environmental health inequalities. Our analysis is based on a densely populated area located in a region in the north of France where the standardized mortality ratio is higher than the national average. Multidimensional composite indices were developed simultaneously from datasets collected for different administrative units, and were used to compare levels of resilience and vulnerability within the geographical area. Joint analysis of composite indices led to the identification of territorial blackspots. Indices could therefore enable stakeholders to prioritize certain administrative units, implementing specific measures according to the specificities of each administrative unit. The study discusses the consequences of the choice of a spatial unit and a geographical area in the assessment of territorial inequalities in health, particularly in terms of accessibility, availability and exhaustivity of data. The selection criteria also depend on the jurisdictional fields of action stakeholders.

Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial Autocorrelation

Identifying and prioritizing hazardous road traffic crash locations is an efficient way to mitigate road traffic crashes, treat point locations, and introduce regulations for area-wide changes. A sound method to identify blackspots (BS) and area-wide hotspots (HS) would help increase the precision of intervention, reduce future crash incidents, and introduce proper measures. In this study, we implemented the operational definitions criterion in the Hungarian design guideline for road planning, reducing the huge number of crashes that occurred over three years for the accuracy and simplicity of the analysis. K-means and hierarchical clustering algorithms were compared for the segmentation process. K-means performed better, and it is selected after comparing the two algorithms with three indexes: Silhouette, Davies–Bouldin, and Calinski–Harabasz. The Empirical Bayes (EB) method was employed for the final process of the BS identification. Three BS were identified in Budapest, based on a three-year crash data set from 2016 to 2018. The optimized hotspot analysis (Getis-Ord Gi*) using the Geographic Information System (GIS) technique was conducted. The spatial autocorrelation analysis separates the hotspots, cold spots, and insignificant areas with 95% and 90% confidence levels.

A two-phase clustering approach for traffic accident black spots identification: integrated GIS-based processing and HDBSCAN model

Identifying black spots effectively and accurately is a pivotal and challenging task to improve road traffic safety. A novel black spot identification model is proposed by integrating the GIS-based processing with hierarchical density-based spatial clustering of applications with noise. Additionally, the optimal clustering parameters are determined based on an internal validation indicator called the density-based clustering validation index to minimize the impact of subjectivity in parameter selection. The model is validated by collecting 3536 accident data from 1 August to 31 October 2020 in Hangzhou, China, and eventually identifies 39 black spots. The results show that: (1) The number of accidents contained in black spots account for 75% of all accidents, while the length of network in the black spots only account for 23.26% of the total road network length. (2) Compared with the conventional density-based spatial clustering of applications with noise model and K-means model, the proposed model achieves the best performance with more accidents gathered per unit road length. (3) The sample survey with 6 onsite of the identified black spots indicates that the proposed model has high recognition accuracy and recommend these sites for further investigation.

Identifying accident black spots based on the accident spacing distribution

The identification of accident black spots is of great significance for the prevention of traffic accidents. Commonly used accident black spot identification methods divide road sections for the analysis of accident data, the direct result of which is the splitting of accident black spots, which affects the results. This paper is based on three years of traffic accident data from the Beijing–Harbin Expressway, including the time and location of traffic accidents, form of the accident fatalities, severe injuries, slight injuries, and property damage only (PDO). To avoid road division effects, an identification method based on the accident spacing distribution is established by using quality control theory. The results show that the average number of accidents per kilometer by the method proposed in this paper is 42, which is much higher than 10, identified by other identification methods. The method proposed in this paper improves the accuracy of the identification results. This method avoids the problem of road segmentation found in other common methods and can more accurately reflect the spatial distribution of traffic accidents. Thus making the identification of accidents more scientific and accurate. • Reviews and summarizes the identification methods of accident black spots. • Uses the Poisson distribution to avoid the division of road sections. • Introduces the concept of quality control management –3 σ principle provide basis for the method. • Compared with other methods, the accuracy of the method proposed in the paper is higher.

Adaptive Kernel Density Estimation for Traffic Accidents Based on Improved Bandwidth Research on Black Spot Identification Model

At present, the total length of accident blackspot accounts for 0.25% of the total length of the road network, while the total number of accidents that occurred at accident black spots accounts for 25% of the total number of accidents on the road network. This paper describes a traffic accident black spot recognition model based on the adaptive kernel density estimation method combined with the road risk index. Using the traffic accident data of national and provincial trunk lines in Shanghai and ArcGIS software, the recognition results of black spots were compared with the recognition results of the accident frequency method and the kernel density estimation method, and the clustering degree of recognition results of adaptive kernel density estimation method were analyzed. The results show that: the accident prediction accuracy index values of the accident frequency method, kernel density estimation method, and traffic accident black spot recognition model were 14.39, 16.36, and 18.25, respectively, and the lengths of the traffic accident black spot sections were 184.68, 162.45, and 145.57, respectively, which means that the accident black spot section determined by the accident black spot recognition model was the shortest and the number of traffic accidents identified was the largest. Considering the safety improvement budget of 20% of the road length, the adaptive kernel density estimation method could identify about 69% of the traffic accidents, which was 1.13 times and 1.27 times that of the kernel density estimation method and the accident frequency method, respectively.