GIS-based analysis of spatial–temporal correlations of urban traffic accidents
Tóm tắt
Understanding the spatial–temporal distribution characteristics of urban road traffic accidents is important for urban road traffic safety management. Based on the road traffic data of Wales in 2017, the spatial–temporal distribution of accidents is formed. The density analysis method is used to identify the areas with high accident incidence and the areas with high accident severity. Then, two types of spatial clustering analysis models, outlier analysis and hot spot analysis are used to further identify the regions with high accident severity. The results of density analysis and cluster analysis are compared. The results of density analysis show that, in terms of accident frequency and accident severity, Swansea, Neath Port Talbot, Bridgend, Merthyr Tydfil, Cardiff, Caerphilly, Newport, Denbighshire, Vale of Glamorgan, Rhondda Cynon Taff, Flintshire and Wrexham have high accident frequency and accident severity per unit area. Cluster analysis results are similar to the density analysis. Finally, the temporal distribution characteristics of traffic accidents are analyzed according to month, week, day and hour. Accidents are concentrated in July and August, frequently in the morning rush hour and at dusk, with the most accidents occurring on Saturday. By comparing the two methods, it can be concluded that the density analysis is simple and easy to understand, which is conducive to understanding the spatial distribution characteristics of urban traffic accidents directly. Cluster analysis can be accurate to the accident point and obtain the clustering characteristics of road accidents.
Tài liệu tham khảo
Cheng, W., & Washington, S. P. (2005). Experimental evaluation of hot spot identification methods. Accident Analysis & Prevention, 37(5), 870–881.
Erdogan, S., Yilmaz, I., Baybura, T., et al. (2008). Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar. Accident Analysis & Prevention, 40(1), 174–181.
Truong, L. I., & Somenahalli, S. (2011). Using GIS to identify pedestrian vehicle crash hot spots and unsafe bus stops. Journal of Public Transportation, 14(1), 99–114.
Ebru Colak, H., Memisoglu, T., et al. (2018). Hot spot analysis based on network spatial weights to determine spatial Statistics of traffic accidents in Rize, Turkey. Arabian Journal of Geosciences, 11(7), 1–11.
Tortum, A., & Atalay, A. (2015). Spatial analysis of road mortality rates in Turkey. Proceedings of the Institution of Civil Engineers-Transport, 168(6), 532–542.
Al-Omari, A., Shatnawi, N., Khedaywi, T., & Miqdady, T. (2020). Prediction of traffic accidents hot spots using fuzzy logic and GIS. Applied Geomatics, 12(2), 149–161.
Shafabakhsh, G. A., & Famili, A. (2017). GIS-based spatial analysis of urban traffic accidents: case study in Mashhad, Iran. Journal of traffic and transportation engineering (English Edition), 4(3), 290–299.
Hayidso, T. H., Gemeda, D. O., & Abraham, A. M. (2019). Identifying road traffic accidents hotspots areas using GIS in Ethiopia: a case study of Hosanna Town. Transport and Telecommunication, 20(2), 123–132.
Li-ya, W., & Xiao-wen, Li. (2019). Urban road traffic accident black spot identification based on GIS. Communications Science and Technology Heilongjiang, 42(11), 212–215.
Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41(3), 359–364.
Peng-Cheng, F., & Xian-yuan, D. (2019). Analysis of common features of accidents prone spots in urban roads network and corresponding application based on spatial clustering. Transportation Science and Technology, 43(4), 85–89.
Jun-hui, Z., & Tuo, S. (2019). Spatial analysis of traffic accidents based on Wave Cluster and vehicle communication system data. EURASIP Journal on Wireless Communications and Networking, 1, 1–10.
Lin, G., Ji-biao, Z., et al. (2018). Analysis of urban road traffic accidents based on improved K-means algorithm. China Journal of Highway and Transport, 31(4), 270–279.
Ke, N., Zhen-sheng, W., et al. (2017). Research on spatial cluster analysis of traffic accident ignorance road network constraints. Geomatics World, 24(6), 50–56.
Yao, L. (2019). Analysis and identification of spatial temporal hotspots and exploration of risk factors for traffic accidents. Zhejiang University.
Briz-Redón, Á., Martínez-Ruiz, F., & Montes, F. (2019). Spatial analysis of traffic accidents near and between road intersections in a directed linear network. Accident Analysis & Prevention, 132, 1–56.
Romano, B., & Jiang Z. (2017). Visualizing traffic accident hotspots—based on spatial—temporal network kernel density estimation [C]//Far-noush Banaei-Kashani. In ACM Sigspatial international conference on advances in Geographic information systems. Redondo Beach, California, USA: Morgan; Claypool does (pp. 98–98).
Ying-zhi, W., & Li-jun, W. (2019). An identification method of traffic accident black point based on street-network spatial-temporal Kernel estimation. Scientia Geographica Sinic, 39(8), 1238–1245.
Jin-lin, C. (2015). Research on identification hotspots in the urban road networks based on the network kernel density estimation method. School of Transportation Southeast University.
Environmental Systems Research Institute. ArcGIS Desktop Help 10.2 Geostatistical Analyst [DB/OL]. Retrieved December 3, 2014, from http://resources.arcgis.com/zhcn/help/main/10.2/index.html.
Noland, R. B., & Quddus, M. (2004). A spatially disaggregate analysis of road casualties in England. Accident Analysis & Prevention, 36(6), 973–984.
Songchitruksa, P., & Zeng, X. (2010). Getis–Ord spatial statistics to identify hot spots by using incident management data. Transportation Research Record, 21(65), 42–51.
Siddiqui, C., & Choik, A.-A. (2012). Macroscopic spatial analysis of pedestrian and bicycle crashes. Accident Analysis & Prevention, 45, 382–391.
Mussone, L., Bassani, M., & Masci, P. (2017). Analysis of factors affecting the severity of crashes in urban road intersections. Accident Analysis & Prevention, 103, 112–122.
Dabbour, E., Easa, S., & Haider, M. (2017). Using fixed-parameter and casual-parameter ordered regression models to identify significant factors that affect the severity of drivers’ groups in vehicle-train collisions. Journal of Accident Analysis & Prevention, 107, 20–30.