GIS-BASED ANALYSIS TO DETECT ROAD ACCIDENT HOTSPOTS USING NETWORK KERNEL DENSITY ESTIMATION
Keywords:
NKDE, SANET, hotspot, geoinformation technologyAbstract
Thailand has one of the highest numbers of road accidents globally. A study of the location, duration, and cause of accidents provided critical information to plan and resolve tribulations. In this study, spatiotemporal analysis by employing geographic information system techniques by using network kernel density estimation as a tool along with a network toolbox was performed to analyze the density of road accidents in the Sri Racha district, Chon Buri province. Data for 2012-2017 were obtained from the road accident data center. Three areas with high accident densities-Laem Chabang City municipality, Sri Racha municipality, and Bowin subdistrict-were investigated because these areas are considered the economic, industrial, and transportation centers of Sri Racha, respectively. Surveillance guidelines for areas with high accident densities can be drawn from the results of this study, and they include planning approaches to prevent road accidents during specific high-risk hours.
References
Aghajania, M.A., Dezfoulianb,c, R.S., Arjroodyb, A.R., and Rezaeid, M. (2017). Applying GIS to identify the spatial and temporal patterns of road accidents using spatial statistics (case study: Ilam province, Iran). Transp Res Proc., 25:2,126-2,138.
Anderson, T.K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Anal Prev., 41:359-364.
Benedek, J., Ciobanub, S.M. and Man, T.C. (2016). Hotspots and social background of urban traffic crashes: a case study in Cluj-Napoca (Romania). Accident Anal Prev., 87:117-126.
Bíl, M., Andrásik, R. and Janoska, Z. (2013). Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation. Accident Anal Prev., 55:265-273.
Deepthi, J.K. and Ganeshkumar, B. (2010). Identification of accident hot spots: a GIS based implementation for Kannur district, Kerala. Int. J. Geomat. Geosci., 1(1):51-59.
Delso, J., Martín, B. and Ortega, E. (2018). A new procedure using network analysis and kernel density estimations to evaluate the effect of urban configurations on pedestrian mobility. The case study of Vitoria-Gasteiz. J. Transp. Geogr., 67:61-72.
Harirforoush, H. and Bellalite, L. (2016). A new integrated GIS-based analysis to detect hotspots: a case study of the city of Sherbrooke. Accident. Anal. Prev., (In Press).
Hashimoto, S., Yoshiki, S., Saeki, R., Mimura, Y., Ando, R. and Nanba, S. (2016). Development and application of traffic accident density estimation models using kernel density estimation. J. Traffic. Transp. Eng. Engl. Ed., 3(3):262-270.
Indian Council of Medical Research, (ICMR). (2009). Development of a feasibility module for road traffic injuries surveillance. JCMR Bull., 39(10-12):41-50.
Mohaymany, A.S., Shahri, M. and Mirbagheri, B. (2013). GIS-based method for detecting high-crash-risk road segments using network kernel density estimation. Geo. Spat. Inf. Sci., 16(2):113-119.
Okabe, A., Satoh, T. And Sugihara, K. (2009). A kernel density estimation method for networks, its computational method and a GIS‐based tool. Int. J. Geogr. Inf. Sci., 23(1):7-32
Okabe, A., Okunuki, K., Satoh, T., Shiode, S., Sugihara, K., Kawabata, M., Takahashi, A. and Morioka, W. (2015). SANET spatial analysis along networks. Available from: sanet.csis.u-tokyo.ac.jp. Access date: December 5, 2019.
Ouni, F. and Belloumi, M. (2018). Spatio-temporal pattern of vulnerable road user’s collisions hot spots and related risk factors for injury severity in Tunisia. Transport Res F-Traf., 56:477-495.
Pleerux, N. (2020). Geographic Information System-based analysis to identify the spatiotemporal patterns of road accidents in Sri Racha, Chon Buri, Thailand. CAST, 20(1):59-70.
Plug, C., Xia, J.C. and Caulfield, C. (2011). Spatial and temporal visualisation techniques for crash analysis. Accident. Anal. Prev., 43:1,937-1,946.
Pulugurtha, S.S., Krishnakumar, V.K. and Nambisan, S.S. (2007). New methods to identify and rank high pedestrian crash zones: an illustration. Accident. Anal. Prev., 39:800-811.
Road Accident Data Center (ThaiRSC). (2019). Statistic of injuries and fatalities all provinces. Available from: www.thairsc.com. Access date: May 10, 2019.
Satria, R. and Castrob, M. (2016). GIS tools for analyzing accidents and road design: A review. Transp Res Proc., 18:242 -247
World Health Organization (WHO). (2018). Road traffic deaths: Data by country 2013. Available from: http://www.who.int. Access date: April 19, 2019.
Xie, Z. and Yan, J. (2008). Kernel density estimation of traffic accidents in a network space. Comput. Environ. Urban. Syst., 32(5):396-406.
Xie, Z. and Yan, J. (2013). Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach. J. Transp. Geogr., 31:64-71
Xu, C., Tarko, A.P., Wang, W. and Lui, P. (2013). Predicting crash likelihood and severity on freeways with real-time loop detector data. Accident Analysis and Prevention 57:30-39.
Yamada, I. and Thill, J.-C. (2004). Comparison of planar and network K-functions in traffic accident analysis. J. Transp. Geogr., 12:149-158.
Yu, R. and Abdel-Aty, M. (2014). Using hierarchical bayesian binary probit models to analyze crash injury severity on high speed facilities with real-time traffic data. Accident. Anal. Prev., 62:161-167.
Zheng, Z., Ahn, S. and Monsere, C.M. (2010). Impact of traffic oscillations on freeway crash occurrences. Accident. Anal. Prev., 42:626-636.
