Traffic accidents poses a serious challenge to authorities in Saudi Arabia attempting to tackle this issue and reduce the number of accidents as well as fatalities caused by these accidents. In Thakaa Challenge for Road Safety, traffic accident data is provided for thirty six thousand accident that occurred on intercity roads between 2017 and April 2019. I attempt to study the data and get some insight if certain locations, time or other factors can be considered risk factors that can help authorities focus their efforts.
in investigating the timing of the accidents. A pattern emerged in which traffic accidents rate was higher in the period from March to June. When considering the timing of the day, it is been shown that accidents number peak at 8 pm and they continue to decline throughout the afternoon until the evening. In terms of the location of the accidents, it is been shown that the top 3 regions in terms of accident numbers account for more than half of all accidents. This does provide the change to make these regions a priority.
Traffic_Data.pynb: is a Jupyter notebook that highlight the steps I took to arrive at my observations and conclusions summerized in the mediuem article below: https://medium.com/@saihat1422/an-investigation-of-traffic-accidents-in-saudi-arabia-d2fd611f52f0
The following libraries were utilized:
- NumPy
- Pandas
- Matplotlib
- Seaborn
This project would not be possible without the efforts of Thakaa who initiated the road saftey challenge and made the data available.