Road Traffic Accident Pattern Analysis with Data Mining

The discovery of Road Traffic Accident (RTA) patterns is vital to formulate mitigation strategies based on the characteristics of RTA. Various studies have applied association rule mining for RTA pattern discovery. However, limited research has conducted a comparative analysis of RTA patterns in different regions. This work is necessary to identify the similarities and differences of RTA characteristics between different regions, especially the developed and developing countries with different RTA trends in fatality rates. This work explored the application of association rule mining (FP-Growth algorithm) to discover and compare the RTA patterns for developed and developing countries. The Orange Data Mining software is used to discover RTA patterns for Ethiopia (12,317 samples) and the UK (United Kingdom) (1,048,575 samples), representing developing and developed countries. Eventually, findings showed similarities and differences in RTA patterns between Ethiopia and the UK. Road characteristics and conditions are the common factors contributing to RTA severity levels, while other crucial factors include the light condition and speed limit. In conclusion, the discovery of RTA patterns in different regions is beneficial to assist in formulating strategies to reduce the risk of RTA. Future work is necessary to discover the RTA patterns from different perspectives, such as seasonal or periodical variations of RTA patterns.