Predicting road traffic accident severity using decision trees and time-series calendar heatmaps

Silva, HCE and Saraee, MH ORCID: https://orcid.org/0000-0002-3283-1912 2019, Predicting road traffic accident severity using decision trees and time-series calendar heatmaps , in: The 6th IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (2019 IEEE CSUDET), 7 - 9 November 2019, Penang, Malaysia.

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Abstract

The European Commission estimates that around 135,000 people are seriously injured on Europe's roads each year. The road traffic injuries are a significant but neglected global general public health problem, needing rigorous attempts for effective and workable prevention. One of the ways to decrease the amount of traffic accidents is to conduct an indepth assessment on the historically documented road traffic incident data and understand the cause of the accidents and factors associated with incident severity. It may provide crucial information for emergency services to evaluate the severity level of accidents, estimate the potential impacts of the casualties, and ultimately it might help to improve the road safety. In this study author is trying to identify the factors that correlate with the slight and serious (including fatal) Road Traffic Accident using Decision Tree classification algorithms using UK STATS19 dataset. Also, author is exploring the possibility of enhancing the knowledge gain from Decision Tree classification algorithms using Time-Series Calendar Heatmap in order to identify hidden temporal patterns. The methodology described in this study offers significant advantages over understanding correlation between hour and month of the accident and the severity of the accident. Although this study is based on a region in North of England, the approach can be applicable to other areas in UK and globally with similar kind of road side accident data. This study found out that combining classification methods like decision tree and time-series calendar heatmaps cam be a useful tool for accurately classifying roadside traffic accidents according to their injury severity.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: The 6th IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (2019 IEEE CSUDET)
Publisher: IEEE
Depositing User: Prof. Mo Saraee
Date Deposited: 25 Nov 2019 09:46
Last Modified: 25 Nov 2019 15:30
URI: http://usir.salford.ac.uk/id/eprint/53164

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