Involvement of Road Users from the Productive Age Group in Traffic Crashes in Saudi Arabia: An Investigative Study Using Statistical and Machine Learning Techniques

Abstract

Road traffic crashes (RTCs) are a major problem for authorities and governments worldwide. They incur losses of property, human lives, and productivity. The involvement of teenage drivers and road users is alarmingly prevalent in RTCs since traffic injuries unduly impact the working-age group (15–44 years). Therefore, research on young people’s engagement in RTCs is vital due to its relevance and widespread frequency. Thus, this study focused on evaluating the factors that influence the frequency and severity of RTCs involving adolescent road users aged 15 to 44 in fatal and significant injury RTCs in Al-Ahsa, Saudi Arabia. In this study, firstly, descriptive analyses were performed to justify the target age group analysis. Then, prediction models employing logistic regression and CART were created to study the RTC characteristics impacting the target age group participation in RTCs. The most commonly observed types of crashes are vehicle collisions, followed by multiple-vehicle and pedestrian crashes. Despite its low frequency, the study area has a high severity index for RTCs, where 73% of severe RTCs include individuals aged 15 to 44. Crash events with a large number of injured victims and fatalities are more likely to involve people in the target age range, according to logistic regression and CART models. The CART model also suggests that vehicle overturn RTCs involving victims in the target age range are more likely to occur as a result of driver distraction, speeding, not giving way, or rapid turning. As compared with the logistic regression model, the CART model was more convenient and accurate for understanding the trends and predicting the involvement probability of the target age group in RTCs; however, this model requires a higher processing time for its development.

The Identification of Motorcyclist Safety Risk among University Students in Yogyakarta, Indonesia

“…10 In previous studies, a lot of information was obtained, one of which explained that traffic accidents are dominated by victims of productive age. 11,12 The highest rate of traffic accidents was observed among the 15-25 years population in one study and the 20-29 years population in another. 13,14 When linked to conditions in Indonesia, the data proves that traffic accidents occur among Senior High School and University students.…”

Machine Learning for Road Traffic Accident Improvement and Environmental Resource Management in the Transportation Sector

“…It is worth noting that the traffic accident data for year 2020 were impacted by the COVID-19 virus and the relative social gathering restrictions [32]. However, rather than seeing this as a limitation, it is an open window to explore other factors rather than human factors, which have been greatly explored [26][27][28][29][30][31]. Also, the use of a year's worth of data in this study is an attempt to take into account the fact that most developing and undeveloped nations are only getting started with accident databases [33,34].…”

Predicting Road Crash Severity Using Classifier Models and Crash Hotspots

“…This illustrates how R.T.C.s are greater in severity and involve more victims in the research area, although having lower occurrence rates than in other regions of the world. Further details about the crash dataset can be found in [63,65].…”

“…Statistics on serious injuries and fatalities in the study area (adopted from[65]). Number of serious injuries/number of RTCs with serious injuries; same was used for the average deaths per RTC.…”