Publication Detail
Multiday Driving Patterns and Motor Carrier Accident Risk: A Disaggregate Analysis
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UCD-ITS-RR-90-09 Research Report |
Suggested Citation:
Kaneko, Tetsuya and Paul P. Jovanis (1990) Multiday Driving Patterns and Motor Carrier Accident Risk: A Disaggregate Analysis. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-90-09
A method has been developed to estimate the relative accident risk posed by different patterns of driving over a multi-day 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 1000 drivers, 9 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 findings of the modeling are that driving patterns over the previous seven days significantly affect accident risk on the eighth day. In general, driving during afternoon and evening hours (e.g. noon to midnight) has the highest accident risk while driving during night and morning hours (e.g. midnight to noon) has lower risk. Consecutive hours driven also has a significant effect on accident risk: the first hour of driving and the ninth and tenth hour of driving have the highest risk. Hours 2 through 8 follow a flattened "u" shape. 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 multi-day 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.
From a data set of over 1000 drivers, 9 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 findings of the modeling are that driving patterns over the previous seven days significantly affect accident risk on the eighth day. In general, driving during afternoon and evening hours (e.g. noon to midnight) has the highest accident risk while driving during night and morning hours (e.g. midnight to noon) has lower risk. Consecutive hours driven also has a significant effect on accident risk: the first hour of driving and the ninth and tenth hour of driving have the highest risk. Hours 2 through 8 follow a flattened "u" shape. 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 multi-day 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.