Author(s)

K. A. D’Souza & S. K. Maheshwari

Abstract

This paper explores the problems of distracted driving for bus drivers at a local transit agency and uncovers factors that may cause the distractions. Data was collected on sources of driver distraction and perceived risks associated with a particular distracting activity along with potentially related independent variables like location, driving hours/week, age, gender, and driving experience. The seven highest distracting activities were categorized into three risk zones using a risk range system derived from the average distracting rating, average distracting duration, and driver’s perception of risk. Multinomial logistic regression was utilized to model each risk zone distracting activity using levels of distraction as the dependent variable and correlating it with the factors as independent /predictor variables. A stepwise procedure included all the selected factors in the model initially; non-significant factors were eliminated until a good fit was achieved with significant factors. The model’s goodness of fit was statistically tested and further verified graphically. The multinomial logistic regression outputs were analyzed for all seven risk zone distracting activities. Due to space limitation, an analysis of the highest risk distracting activity involving passenger using mobile phone is included in the paper. The results revealed that the common sources of driver distractions were due to passenger-related activities. The male drivers are more likely than female drivers to get distracted by passengers, while female drivers are more likely to get distracted by the ticket machine than male drivers. Older drivers are less likely to get distracted by the ticket machine and passenger-related activities, although more driving experience increased the likelihood of distraction by passengers and ticket machines. The drivers with higher weekly driving hours are less likely to get distracted by ticket machines and climate controls. The recommendations made on the basis of the results could be used as a potential training tool to mitigate driver distraction and improve bus transit performance. Keywords: sources of transit bus driver distraction, modelling bus driver distraction, multinomial logistic regression, stepwise procedure, predicting driver distraction risk, risk range system, risk zone.

Keywords

sources of transit bus driver distraction, modelling bus driverdistraction, multinomial logistic regression, stepwise procedure, predictingdriver distraction risk, risk range system, risk zone.