Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach
2. Data Description
3.1. Model Description
3.2. Data Pre-Processing
3.3. Metrics Evaluation
4.1. Model Development
- Sequential minimal optimization (SMO).
- Random forest.
- Artificial neural network (ANN).
- Logistic regression.
- Naïve Bayes.
4.2. Model Performance
4.3. Attribute Evaluation Analysis
- Crash type: ‘Vehicle–pedestrian’ type was the strongest predictor of fatal road crashes. ‘Truck–bike’ type was the second strongest predictor of fatal road crashes within the crash type category.
- Injury severity level: The severity level variable was a major contributor to fatal crashes.
- Spatial cluster ID: Densely populated areas and the presence of major highway crossings proved to be correlated with increased traffic fatalities.
- Hour of road crash: Time of the day, more specifically 3 am, was highly correlated with fatality occurrence.
- Day of the week. Friday and Sunday were the two strong factors affecting the occurrence of fatal road injuries.
- Road type: Motorways are the main road types correlated with fatal crashes.
- AM-PM and day and month of the year were the least influencing factors on the fatality of the road crash.
Conflicts of Interest
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|Day of the Week||Monday–Sunday|
|Hour of Crash||0–23|
|Crash Type||Vehicle–Vehicle, Vehicle–Truck, Vehicle–Pedestrian, Vehicle–Motorcycle, Vehicle–Barrier, Truck–Truck, Truck–Motorcycle, Truck–Barrier, Motorcycle–Motorcycle, Motorcycle–Barrier, Other|
|Injury Severity Level||No Apparent-Injury, Minor Injury, Serious Injury|
|Road Type||Motorway, Trunk, Primary, Secondary, Tertiary, Unclassified|
|Spatial Cluster ID||1–10|
|Fatality occurrence||Fatal, Not Fatal|
|Bagging J48 100 decision trees||0.464||0.382||0.4365|
|Vote SMO with Bagging J48||0.511||0.402||0.4882|
|Vote SMO with Bagging J48|
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Ghandour, A.J.; Hammoud, H.; Al-Hajj, S. Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach. Int. J. Environ. Res. Public Health 2020, 17, 4111. https://doi.org/10.3390/ijerph17114111
Ghandour AJ, Hammoud H, Al-Hajj S. Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach. International Journal of Environmental Research and Public Health. 2020; 17(11):4111. https://doi.org/10.3390/ijerph17114111Chicago/Turabian Style
Ghandour, Ali J., Huda Hammoud, and Samar Al-Hajj. 2020. "Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach" International Journal of Environmental Research and Public Health 17, no. 11: 4111. https://doi.org/10.3390/ijerph17114111