Abstract
The identification and classification of accident black spots on urban roads is a key element of road safety research. To solve the problems caused by the randomness of accident occurrences and the unclear classification of accident black spots by the traditional model, we propose a method that can quickly identify and classify accident black spots on urban roads: a combined grey Verhuls–Empirical Bayesian method. The grey Verhuls model is used to obtain the predicted/expected numbers of accidents at accident hazard locations, and the empirical Bayesian approach is used to derive two accident black spot discriminators, a safety improvement space and a safety index (SI), and to classify the black spots into two, three, four and five levels according to the range of the SI. Finally, we validate this combined method on examples. High-quality and high-accuracy data are obtained from the accident collection records of the Ningbo Jiangbei District from March to December 2020, accounting for 90.55% of the actual police incidents during this period. The results show that the combined grey Verhuls–Empirical Bayesian method can identify accident black spots quickly and accurately due to the consideration of accident information from the same types of accident locations. The accident black point classification results show that the five-level rating of accident black points is most reasonable. Our study provides a new idea for accident black spot identification and a feasible method for accident black spot risk level classification.
Highlights
Road traffic safety is a world-recognized traffic problem, and according to the findings of the World Health Organization (WHO), road traffic accidents are the eighth leading cause of death worldwide
The accident black spots were divided into the following levels: (a) In terms of accident black spot identification, accident black spots and non-accident black spots were identified for the accident data of 10 hidden accident sections over months, and from the results, 12 non-accident black spots and 88 accident black spots were detected for a total of 100 data points, which means that the model can be used for accident black spot identification
(b) In terms of accident black spot classification, the rationality of the proportions of the identified accident black spots with grades I, II and III in the two-grade assessment, three-grade assessment, four-grade assessment and five-grade assessment were discussed, and the analysis results showed that the five-grade assessment > the four-grade assessment > the three-grade assessment > the two-grade assessment and that the fivegrade assessment of accident black spots is most suitable for the actual situation, which provides a new way of thinking regarding accident black spot classification
Summary
Road traffic safety is a world-recognized traffic problem, and according to the findings of the World Health Organization (WHO), road traffic accidents are the eighth leading cause of death worldwide. The Global Status Report on Road Safety 2018, launched by the WHO in December 2018, highlights that the number of annual road traffic deaths has reached 1.35 million. Road traffic injuries are the leading killer of people aged 5 to years. China shows [2] that 265,204 road traffic accidents occurred nationwide in 2019, resulting in 73,484 deaths, 304,919 injuries and USD 156.19 million in direct property losses. A reasonable assessment of road traffic safety is a necessary prerequisite and basic condition for reducing road traffic accidents and accident losses, so the study of accident black spot classification is of great significance. Accident black spots can be intersections, road sections and other areas and can refer to different contents according to the given research object
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
