Volume List  / Volume 7 (3)

Article

IDENTIFICATION OF ROAD CRASH BLACK-SITES USING GEOGRAPHICAL INFORMATION SYSTEM

DOI: 10.7708/ijtte.2017.7(3).07


7 / 3 / 368-380 Pages

Author(s)

Rouzbeh Shad - Civil Department, Engineering Faculty, Ferdowsi University of Mashhad, Iran -

Shahriar Rahimi - Civil Department, International Campus, Ferdowsi University of Mashhad, Iran -


Abstract

It is a crucial task to reduce road crashes by performing analyses and taking appropriate countermeasures to save lives. It has been a major issue for many people and government to reduce the amount of road collisions especially in Iran, since it could be a great threat to this country. Identification of crash black-sites is one of the most important fields in road safety studies. Many highway agencies have been using Geographical Information System (GIS) for analyzing crash data. The GIS based application integrates the information collection capabilities with the visualization. In this paper, Incident features like location, date, type of vehicle involved, and number of persons injured or died are imported in the GIS database. Then, Kernel density is to apply on the prepared data. The main objective is to specify road crash black-spots considering all types of traffic crashes based on their severity using Kernel Density through GIS environment. The results show that more severe crashes take place further from the cities than accidents with only property damages. Also, overlaying all crash types using the suggested SI (severity index) method will generate crash black-sites map which policy makers can use to determine accident-prone zones and take appropriate interventions.


Download Article

Number of downloads: 188


References:

Alam, M.; Ahsan, H.M. 2014. Identification and Characterization of Hazardous Road Locations on Dhaka-Chittagong National Highway, Global Journal of Research In Engineering 13(4): 17-26.

 

Anderson, T. 2007. Comparison of spatial methods for measuring road accident ‘hotspots’: a case study of London, Journal of Maps 3(1): 55-63.

 

Anderson, T.K. 2009. Kernel density estimation and K-means clustering to profile road accident hotspots, Accident Analysis & Prevention 41(3): 359-364.

 

Ayati, E.; Vahedi, J.R. 2007. Developing Bridge Safty Index Model for Iran, Engineering Faculty Journal of Ferdowsi University of Mashhad 19: 135-51.

 

Bailey, T.C.; Gatrell, A.C. 1995. Interactive spatial data analysis. Essex: Longman Scientific & Technical. USA. 413 p.

 

Bhalla, P.; Tripathi, S.; Palria, S. 2014. Road Traffic Accident Analysis of Ajmer City Using Remote Sensing and GIS Technology. In Proceedings of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(8): 1455-1462.

 

Çela, L.; Shiode, S.; Lipovac, K. 2013. Integrating GIS and spatial analytical techniques in an analysis of road traffic accidents in Serbia, International Journal for Traffic and Transport Engineering 3(1): 1-15.

 

Chainey, S.; Ratcliffe, J. 2013. GIS and crime mapping. John Wiley & Sons. USA. 442 p.

 

Eluru, N.; Bhat, C.R.; Hensher, D.A. 2008. A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes, Accident Analysis & Prevention 40(3): 1033-1054.

 

Erdogan, S.; Yilmaz, I.; Baybura, T.; Gullu, M. 2008. Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar, Accident Analysis & Prevention 40(1): 174-181.

 

Fotheringham, A.S.; Brunsdon, C.; Charlton, M., 2000. Quantitative Geography: Perspectives on Spatial Data Analysis. SAGE Publications. Ireland. 267 p.

 

Hauer, E. 1996 Identification of sites with promise, Transportation Research Record: Journal of the Transportation Research Board 1542: 54-60.

 

Hu, S.R.; Li, C.S.; Lee, C.K. 2010. Investigation of key factors for accident severity at railroad grade crossings by using a logit model, Safety science 48(2): 186-194.

 

Huang, H.; Chin, H.; Haque, M. 2009. Empirical evaluation of alternative approaches in identifying crash hot spots: naive ranking, empirical Bayes, and full Bayes methods, Transportation Research Record: Journal of the Transportation Research Board (2103): 32-41.

 

Kumaresan, V.; Vasudevan, V.; Nambisan, S. 2009. Development of a GIS-based Traffic Safety Analysis System. In Proceedings of the Annual ESRI International User Conference, 13-17.

 

Longley, P.; Goodchild, M.F.; Maguire, D.; Rhind, D. 2010. Geographic information systems and science. John Wiley & Sons. USA. 526 p.

 

Pino‐Díaz, J.; Jiménez‐Contreras, E.; Ruíz‐Baños, R.; Bailón‐Moreno, R. 2012. Strategic knowledge maps of the techno‐scientific network (SK maps), Journal of the Association for Information Science and Technology 63(4): 796-804.

 

Potoglou, D. 2008. Vehicle-type choice and neighbourhood characteristics: An empirical study of Hamilton, Canada, Transportation Research Part D: Transport and Environment 13(3): 177-186.

 

Prasannakumar, V.; Vijith, H.; Charutha, R.; Geetha, N. 2011. Spatio-temporal clustering of road accidents: GIS based analysis and assessment, Procedia-Social and Behavioral Sciences 21: 317-325.

 

Quddus, M.A.; Wang, C.; Ison, S.G. 2009. Road traffic congestion and crash severity: econometric analysis using ordered response models, Journal of Transportation Engineering 136(5): 424-435.

 

RMTO. 2015. Statistical Yearbook of Road Maintenance and Road Transportation Organization. Ministry of Roads and City Planning. Iran.

 

Rodrigues, D.S.; Ribeiro, P.J.G.; da Silva Nogueira, I.C. 2015. Safety classification using GIS in decision-making process to define priority road interventions, Journal of Transport Geography 43: 101-110.

 

Sorensen, M.; Elvik, R. 2007. Black Spot Management and Safety Analysis of Road Networks. Institute of transport economics. Norway. 118 p.

 

Steenberghen, T.; Dufays, T.; Thomas, I.; Flahaut, B. 2004. Intra-urban location and clustering of road accidents using GIS: a Belgian example, International Journal of Geographical Information Science 18(2): 169-181.

 

Sun, Y.; Chang, H.; Miao, Z.; Zhong, D. 2012. Solution method of overtopping risk model for earth dams, Safety science 50(9): 1906-1912.

 

Turton, I.; Openshaw, S. 2001. 2 Methods for automating the geographical analysis of crime incident data, Mapping and Analysing Crime Data: Lessons from Research and Practice 11-26.

 

Wang, C.; Quddus, M.A.; Ison, S.G. 2011. Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model, Accident Analysis & Prevention 43(6):1979-1990.

 

WHO. 2015. Global status report on road safety. World Health Organization. Switzerland. 340 p.