Skip to main content

Research Repository

Advanced Search

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

Silva, HCE; Saraee, MH

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


Authors

HCE Silva



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.

Citation

Silva, H., & Saraee, M. (2019, November). Predicting road traffic accident severity using decision trees and time-series calendar heatmaps. Presented at The 6th IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (2019 IEEE CSUDET), Penang, Malaysia

Presentation Conference Type Other
Conference Name The 6th IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (2019 IEEE CSUDET)
Conference Location Penang, Malaysia
Start Date Nov 7, 2019
End Date Nov 9, 2019
Acceptance Date Sep 14, 2019
Deposit Date Nov 25, 2019
Publicly Available Date Nov 25, 2019
Publisher URL http://csudet2019.com/
Additional Information Event Type : Conference

Files






You might also like



Downloadable Citations