Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning techniques - Balamurugan Shandhana Rashmi; Sankaran Marisamynathan (International Journal of Transportation Science and Technology)
Speeding is one of the most common aberrant driving behaviors among the driving population. Although research on speeding behavior among drivers has increased over the decades, little is known about the motivating factors associated with speeding behavior among long-haul truck drivers (LHTDs), especially in developing nations like India. This study aims to develop a prediction model for speeding behavior and to identify the contributory factors and their influential patterns underlying speeding behavior among LHTDs in India. A cross-sectional study was conducted among LHTDs in Salem City, Tamil Nadu, India. The data were collected through face-to-face interviews using a questionnaire encompassing socio-demographic, work, vehicle, health-related lifestyle, and speeding-related characteristics. A total of 756 valid samples were collected and utilized for analysis purposes. While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. The analysis results showed that RF demonstrated superior performance in predicting speeding behavior over other competing algorithms with accuracy (0.80), F1 score (0.77), and AUROC (0.81). From the befitting RF model, the importance of factors contributing to speeding behavior among LHTDs was determined through the variable importance plot. Pressured delivery of goods, sleeping duration per day, age of truck, size of truck, monthly income, driving experience, driving duration per day, and age of the driver were identified as the eight topmost critical factors contributing to speeding behavior among LHTDs. Based on the developed RF model, the hidden relationships behind identified critical factors in relation to the speeding behavior were investigated using partial dependence plots (PDPs). The outcomes of this research will be useful for road safety authorities and Indian trucking industries to frame suitable policies and to introduce effective strategies for mitigating speeding behavior among LHTDs to promote road safety.