TY - JOUR
T1 - Predicting Damage Incidents, Fines, and Fuel Consumption from Truck Driver Data
T2 - A Study from the Netherlands
AU - Driessen, Tom
AU - Dodou, Dimitra
AU - Waard, Dick de
AU - Winter, Joost de
N1 - Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2023.
PY - 2024/7
Y1 - 2024/7
N2 - Trucks are disproportionately involved in fatal traffic accidents and contribute significantly to CO2 emissions. Gathering data from trucks presents a unique opportunity for estimating driver-specific costs associated with truck operation. Although research has been published on the predictive validity of driver data, such as in the contexts of pay-how-you-drive insurance and naturalistic driving studies, the investigation into how telematics data relate to the negative consequences of truck driving remains limited. In the present study, driving data from 180 truck drivers, collected over a 2-year period, were examined to predict damage incidents, traffic fines, and fuel consumption. Correlation analysis revealed that the number of fines and damage incidents could be predicted based on the number of harsh braking events per hour of driving, whereas fuel consumption was predicted by engine torque exceedances. Our analysis also sheds light on the impact of covariates, including the engine capacity of the truck operated and time of day, among others. We conclude that the damage incidents and fines incurred by truck drivers can be predicted not only from their number of harsh decelerations but also through driving demands that extend beyond the driver’s immediate control. It is recommended that transportation companies adopt a systemic approach to mitigating truck-driving-related expenses.
AB - Trucks are disproportionately involved in fatal traffic accidents and contribute significantly to CO2 emissions. Gathering data from trucks presents a unique opportunity for estimating driver-specific costs associated with truck operation. Although research has been published on the predictive validity of driver data, such as in the contexts of pay-how-you-drive insurance and naturalistic driving studies, the investigation into how telematics data relate to the negative consequences of truck driving remains limited. In the present study, driving data from 180 truck drivers, collected over a 2-year period, were examined to predict damage incidents, traffic fines, and fuel consumption. Correlation analysis revealed that the number of fines and damage incidents could be predicted based on the number of harsh braking events per hour of driving, whereas fuel consumption was predicted by engine torque exceedances. Our analysis also sheds light on the impact of covariates, including the engine capacity of the truck operated and time of day, among others. We conclude that the damage incidents and fines incurred by truck drivers can be predicted not only from their number of harsh decelerations but also through driving demands that extend beyond the driver’s immediate control. It is recommended that transportation companies adopt a systemic approach to mitigating truck-driving-related expenses.
KW - driver
KW - freight systems
KW - safety
KW - safety and human factors
KW - truck and bus safety
KW - trucking industry research
KW - trucks
UR - http://www.scopus.com/inward/record.url?scp=85179314452&partnerID=8YFLogxK
U2 - 10.1177/03611981231211897
DO - 10.1177/03611981231211897
M3 - Article
AN - SCOPUS:85179314452
SN - 0361-1981
VL - 2678
SP - 1026
EP - 1042
JO - Transportation Research Record
JF - Transportation Research Record
IS - 7
ER -