Comparisons of Predictive Power for Traffic Accident Involvement; Celeration Behaviour versus Age, Sex, Ethnic Origin, and Experience

Abstract

Driver celeration behaviour theory (DCBT) assumes that risk for a driver of causing a road crash is linearly related to speed change in any given moment and that the speed change variable (celeration) captures all risk (all vehicle control movements can be measured as acceleration). When sampling driver behaviour, the celeration variable is calculated as the average of all absolute values of acceleration when the vehicle is moving. DCBT predicts that no other variable can be a stronger predictor of (the same set of) traffic accident involvements than celeration, given equal reliability of the predictors. Also, other predictors, regardless of which ones, should associate with celeration in ways that are similar to how they correlate with accidents. Predictions were tested in a sample of bus drivers, against variables with reliabilities close to 1 (age, sex, experience, ethnic origin), which are not necessarily optimal predictors for testing but were the only predictors available. The results were largely as predicted from theory. The principles for testing the kind of predictions made from celeration theory were discussed, outlining the importance of a larger number of variables, preferably with repeated measurements.

Preliminary driving style classification of the professional drivers

“…The correlation coefficient ranges from -0.18 to -0.12 (p<0.05). The results of the research presented in work [1] confirm the weak correlation between the driver's experience and the values of acceleration. The correlation studies presented in [1,16,39] show that the experience of a city bus driver does not significantly influence the number of traffic violations, accidents and dangerous situations on the road.…”

The Influence of Socio-demographics Background on the Driving Behavior: A Short Review

“…From the review of literature, it was found that among the data collection method used in the published articles were surveys, analysis of traffic accident or police report data, and using virtual reality experiments. Af Wåhlberg (2018), Matthews and Norris (2002), Bener et al (2013), Factor et al (2012), Papacostas and Synodinos (1988), Patil et al (2006), andPutranto andAlyandi (2019) carried out surveys to understand driving behavior. This is called the Driver Behaviour Questionnaire (DBQ) that consists of lapses, mistakes, and slips.…”

“…Driving behavior studies of occupational drivers were also conducted to assess the drivers' behavior (Af Wåhlberg, 2018;Dorothy et al, 1998;Kurniasih, 2019). Another interesting topic of driving behavior is the perception of drivers to police bias while stopping them whereas minor ethnicities (i.e.…”