A hybrid method based on P and P′ control chart for identifying hotspots

Abstract

The terms “hotspots,” “blackspot,” or “high‐risk” are normally used for an area on a road where the number of car accidents are far beyond the normal level and, more importantly, the causes of the incident always link to one or many specific reasons. Identifying these spots is helpful and may assist people in authority to implement corrective actions. To do the aforementioned, some decision‐support tools could be useful since they enable us to monitor the indicators that may lead to a car accident, same as the procedure engineers use to control production lines in factories. In this regard, control charts are utilized to identify blackspots. Although the method is low cost, and provides quick detection and statistical reliability level and, more importantly, uses online applications; however, it advised that the rate quality control method could be used on roads with the same geometry and traffic conditions. This research develops a hybrid method, based on accident type, for improving rate quality control method to overcome the shortcomings. Furthermore, the suggested framework considers crash severity and traffic volume that have not been taken into account in the rate quality control method yet. The case study evaluates how this framework is successful to identify blackspots, and based on the result in the real environment, it reveals that the proposed method could detect and identify the hotspots.

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

“…Sci. 2024, 14,2108 This experiment requires the use of street view image semantic segmentati the corresponding road intersection data. There are 2519 intersections in intersections are mainly divided into two categories: one is traffic black spot i and the other is non-traffic black spot intersections.…”

“…The emergence of geographic big data and the rapid development of machine learning and deep learning make it possible to portray the built environment of urban roads from street view image data [11][12][13]. In real life, traffic accidents are also more likely to occur on densely populated streets with crowded and dilapidated buildings, without better road infrastructure, and with missing road markings and blurred and uneven road surfaces [14][15][16]. These features are all part of the built environment of urban roads, which can be described by street image data.…”

Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation

“…The results showed that it was beneficial to divide the accident site by using the heat map analysis method of kernel density estimation. Jahan et al [17] developed a hybrid method, based on accident type, for improving the rate quality control method to overcome the shortcomings. According to the case study and based on the results in the real environment, the proposed method could detect and identify the accident hotspots.…”