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Development of a GIS-based traffic accident and road database management system

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dc.contributor.author Karunarathna, CJ
dc.contributor.author Rengerasu, TM
dc.contributor.editor Gunaruwan, TL
dc.date.accessioned 2022-04-25T04:55:18Z
dc.date.available 2022-04-25T04:55:18Z
dc.date.issued 2016-06
dc.identifier.citation Karunarathna, C.J., & Rengerasu, T.M. (2016). Development of a GIS-based traffic accident and road database management system [Extended Abstract]. In T.L. Gunaruwan (Ed.), Proceedings of 1st International Conference on Research for Transport and Logistics Industry 2016 (pp. 75-78). Sri Lanka Society of Transport and Logistics. https://slstl.lk/r4tli-2016/ en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/17676
dc.description This research was to develop a Traffic Accident Analysis System (TAAS) to aid in the identification of accident black spots and develop a statistical model to predict traffic accident severity. TAAS was developed as a set of python tools and deployed as a toolbox in ArcGIS© 10.X. There were all together more than 252,251 traffic accidents (from 2008-2014) reported in Sri Lanka. TAAS consists of data for 20,041 traffic accidents reported in the Southern province of Sri Lanka over eight years (2008-2014). All relevant attributes of traffic accidents in the possession of the traffic police were included in TAAS. (Traffic Police Statistics in Sri Lanka 2014). According to the World Health Organization (WHO) [1], more than 1.3 million people die each year in traffic accidents and more than 50 million are injured worldwide (WHO 2012). Sri Lanka traffic police analyse traffic accidents through a software called MAAP. The collected data are not properly used for analysis because it cannot be done in a user-friendly manner. As a solution to this weakness, a GIS-based accident analysis system [2] which links a great volume of accidents was developed. As for the second objective of this study a logistic regression model was developed to predict the traffic accident severity. 2,802 serious and fatal traffic accidents were used in the model. Out of eight independent variables used, three were found significantly associated with traffic accident severity: „Time‟, „Road Surface Condition‟ and „Days of Week‟. en_US
dc.language.iso en en_US
dc.publisher Sri Lanka Society for Transport and Logistics en_US
dc.relation.uri https://slstl.lk/r4tli-2016/ en_US
dc.subject Traffic accidents analysis en_US
dc.subject GIS analysis en_US
dc.subject Python tool box en_US
dc.title Development of a GIS-based traffic accident and road database management system en_US
dc.type Conference-Extended-Abstract en_US
dc.identifier.faculty Engineering
dc.identifier.department Department of Transport and Logistics Management en_US
dc.identifier.year 2016 en_US
dc.identifier.conference 1st International Conference on Research for Transport and Logistics Industry 2016 en_US
dc.identifier.place Colombo en_US
dc.identifier.pgnos pp. 75-78 en_US
dc.identifier.proceeding Proceedings of 1st Conference on Research for Transport and Logistics Industry 2016 en_US


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