Searching possible accident black spot locations with accident analysis and GIS software based on GPS coordinates

Abstract

There are several methods for the analysis of road accidents in a road network. In Hungary from 2011 GPS coordinates are used to identify the location of personal injury accidents. This method significantly improves the display of locations of accidents on the map, which can be then analyzed using GIS tools. Accident black spots are the most dangerous places in road networks identified by the density of the accidents in the network. One of the analysis methods is the accident density searching. The methods and algorithms used in some software may show differences in relation to one another. The aim of this research is comparing two applications by investigating the local road network in GyĘr. The analysis was made using the WEB-BAL accident analysis software using the density-based spatial clustering of applications with noise procedure and the QGIS software using the kernel density estimation method. The former is the official accident database and online software used for accident investigations and the latter is an open source geographic information system. The results are visualized in accident density plots and black spot maps.

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam

“…Meanwhile, DBSCAN only creates the shape of the clusters and does not mention the density of the clusters. The density differences among clusters are not highlighted (Hegyi, Borsos, and Koren 2017). Also, DBSCAN cannot group well events with changed densities is the biggest drawback of this method (Qiu, Xu, and Bao 2016).…”

Peer Review #1 of "Analysis of historical road accident data supporting autonomous vehicle control strategies (v0.2)"

“…There are some general attributes of accident black spots to overcome the conceptual confusion. These are usually well-defined sections or intersections of the public road network, where road accidents are historically concentrated (Elvik, 2008;Delorme and Lassarre, 2014;Murray et al, 2012;Montella et al, 2013;Hegyi et al, 2017). Nowadays, road accidents are monitored by the governments and all data about accidents are stored in large, reliable and partially public databases (without any personal information about the participants).…”

Geospatial Analysis of Road Traffic Crashes and Emergency Response Optimization in Kano Metropolis, Nigeria

“…Many studies have been conducted around the world using various approaches and methodologies to solve problems related to the occurrence of road tra c crashes, their causes, consequences, and emergency response in order to achieve the SDG, such as Huang & Pan, 2007;Anderson 2009;Durduran, 2010;Lloyd 2010;Polat & Durduran 2011;Plug, Xia, and Caul eld. 2011;Shekhar et al 2011;Han, Pei, and Kamber, 2011;Plug, Xia, and Caul eld, 2011;Han, Pei, and Kamber, 2011;Han, Pei, and Kamber, 2011;Han, Pei, and Kamber, 2011;Han, Pei, Dai, 2012;MOT, 2012;Ponnaluri, 2012;Dai, 2012;Mirbagheri, 2013;ela, Shiode, and Lipovac 2013;Yang, Lu, & Wu, 2013;Xie and Yan 2013;Mohaymany, Shahri, and Mirbagheri 2013;Timmermans et al, 2015;Xuan 2015;Choudhary, Ohri, and Kumar 2015;Qiu, Xu, and Bao 2016;Harirforoush and Bellalite 2016;Satria & Castro, 2016;Sandhu et al 2016;Corazza et al, 2017;Harirforoush 2017;Aghajani et al, 2017;Shafabakhsh, Famili & Bahadori 2017;Hegyi, Borsos, and Koren 2017;Amorim, Ferreira & Couto, 2017;Vemulapalli et al 2017;Phong, 2018;Iyanda, 2019;Chung, 2019;…”