Data Mining on Motorcyclists’ Behaviour among Commuting Workers in Malaysia
Keywords:Commuting accident, Risk-taking, Human factors, Helmet, Turn signal, Motorcyclist behaviour
Commuting is a process to travel from one place to another. In the context of workers, they need to commute from their residences to their workplace or otherwise, usually every day. However, the risk to involve in road crashes during commuting is high for such a group in Malaysia. According to statistics from the Social Security Organisation (SOCSO), the trend of commuting accidents in Malaysia has consistently increased year after year. Compared to 17,609 cases in 2003, the number of cases rose almost double to 33,319 cases in 2017, and most of the travellers involved commuted by motorcycle. This study aims to explore the risky riding behaviours at signalised intersections among commuting workers on motorcycles. A total of 33,122 workers commuting by motorcycles were observed at six intersections during six days in Terengganu. Two risk behaviours (helmet non-use and turn signal neglect) were observed together with demographic and contextual factors. Data mining approach – decision tree models for helmet non-use and turn signal neglect were performed based on a 10-fold cross-validation technique, with the demographic and contextual explanatory factors. The results showed that non-use helmet among commuting workers significantly related to carrying passenger, gender, day of the week, and time of the day. Predicted factors related to turn signal neglect behaviour included carrying passenger, gender, type of junction, number of lanes, and day of the week. Findings from this research can help safety department in workplaces to include awareness regarding these behaviours in their training program.
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