Multiday driving patterns and motor carrier accident risk: a disaggregate analysis.
A method has been developed to estimate the relative accident risk posed by different patterns of driving over a multiday period. The procedure explicitly considers whether a driver is on duty or off duty for each half hour of each day during the period of analysis. From a data set of over 1,000 drivers, nine distinct driving patterns are identified. Membership in the patterns is determined exclusively by the pattern of duty hours for seven consecutive days; for some drivers an accident occurred on the eighth day while others had no accident, therefore each pattern can be associated with a relative accident risk. Additional statistical modeling allowed the consideration, in addition to driving pattern, of driver age, experience with the firm, hours off duty prior to the last trip and hours driving on the last trip (either until the accident or successful completion of the trip). The finding of the modeling is that driving patterns over the previous seven days significantly affect accident risk on the eighth day. In general, driving during the early and late morning (e.g., midnight to 10 A.M.) has the highest accident risk while all seven other multiday patterns had indistinguishable risk. Consecutive hours driven also has a significant effect on accident risk: the first hour through the fourth hour having the lowest risk with a fluctuating increase in risk to a maximum beyond nine hours. Driver age and hours off duty immediately prior to a trip do not appear to affect accident risk significantly. These findings quantitatively assess the relative accident risk of multiday driving patterns using data from actual truck operations. Further research is recommended in the areas of refining model structures, adding explanatory variables (such as highway type), and testing more complex models.
Kaneko
T
T
Sato Kogyo Company Ltd., Tokyo, Japan.
Jovanis
P P
PP
D000062:Accidents, Occupational / Q000706:statistics & numerical data*; D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D016000:Cluster Analysis; D006801:Humans; D016015:Logistic Models; D016272:Occupational Health; D012106:Research; D012307:Risk Factors; D014481:United States