Driving behaviors associated with emergency service vehicle crashes in the U.S. fire service

D. P. Bui, Chengcheng Hu, A. M. Jung, K. M. Pollack Porter, Stephanie Griffin, D. D. French, S. Crothers, Jefferey L Burgess

Research output: Contribution to journalArticle

Abstract

Objective: Emergency service vehicle incidents are a leading cause of firefighter fatalities and are also hazardous to civilian road users. Modifiable driving behaviors may be associated with emergency service vehicle incidents. The goal of this study was to use telematics to identify driving behaviors associated with crashes in the fire service. Methods: Forty-three emergency service vehicles in 2 fire departments were equipped with telematics devices (12 in Department A and 31 in Department B). The devices collected vehicle coordinates, speed, and g forces, which were monitored for exceptions to driving rules established by the fire departments regarding speeding, harsh braking, and hard cornering. Fire department administrative reports were used to identify vehicles involved in crashes and merged with daily telematics data. Penalized logistic regression was used to identify driving rules associated with crashes. Least absolute shrinkage and selection operator (LASSO) regression was used to generate a telematics-based risk index for emergency service vehicle incidents. Results: Nearly 1.1 million km of driving data and 44 crashes were recorded among the 2 departments during the study. Harsh braking was associated with increased odds of crash in Department A (odds ratio [OR] = 2.22; 95% confidence interval [CI], 1.09–4.51) and Department B (OR = 1.55; 95% CI, 1.12–2.15). For every kilometer of nonemergency speeding, the odds of crash increased by 35% in Department A (OR = 1.35; 95% CI, 1.03–1.77) and by over 2-fold in Department B (OR = 2.09; 95% CI, 1.19–3.66). In Department B, hard cornering (OR = 1.14; 95% CI, 1.03–1.26) and emergency speeding (OR = 1.65; 95% CI, 1.06–2.57) were also associated with increased odds of crash. The final LASSO risk index model had a sensitivity of 73% and specificity of 57%. Conclusions: Harsh braking and excessive speeding were driving behaviors most associated with crash in the fire service. Telematics may be a useful tool for monitoring driver safety in the fire service.

Original languageEnglish (US)
JournalTraffic Injury Prevention
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Emergency services
traffic behavior
telematics
Fires
Emergencies
Odds Ratio
Confidence Intervals
confidence
fire department
Braking
incident
Firefighters
Equipment and Supplies
regression
road user
Logistics
Logistic Models
Safety
Sensitivity and Specificity
driver

Keywords

  • crash prevention
  • driving behaviors
  • Emergency vehicles
  • firefighters
  • risk index
  • telematics

ASJC Scopus subject areas

  • Safety Research
  • Public Health, Environmental and Occupational Health

Cite this

Driving behaviors associated with emergency service vehicle crashes in the U.S. fire service. / Bui, D. P.; Hu, Chengcheng; Jung, A. M.; Pollack Porter, K. M.; Griffin, Stephanie; French, D. D.; Crothers, S.; Burgess, Jefferey L.

In: Traffic Injury Prevention, 01.01.2018.

Research output: Contribution to journalArticle

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abstract = "Objective: Emergency service vehicle incidents are a leading cause of firefighter fatalities and are also hazardous to civilian road users. Modifiable driving behaviors may be associated with emergency service vehicle incidents. The goal of this study was to use telematics to identify driving behaviors associated with crashes in the fire service. Methods: Forty-three emergency service vehicles in 2 fire departments were equipped with telematics devices (12 in Department A and 31 in Department B). The devices collected vehicle coordinates, speed, and g forces, which were monitored for exceptions to driving rules established by the fire departments regarding speeding, harsh braking, and hard cornering. Fire department administrative reports were used to identify vehicles involved in crashes and merged with daily telematics data. Penalized logistic regression was used to identify driving rules associated with crashes. Least absolute shrinkage and selection operator (LASSO) regression was used to generate a telematics-based risk index for emergency service vehicle incidents. Results: Nearly 1.1 million km of driving data and 44 crashes were recorded among the 2 departments during the study. Harsh braking was associated with increased odds of crash in Department A (odds ratio [OR] = 2.22; 95{\%} confidence interval [CI], 1.09–4.51) and Department B (OR = 1.55; 95{\%} CI, 1.12–2.15). For every kilometer of nonemergency speeding, the odds of crash increased by 35{\%} in Department A (OR = 1.35; 95{\%} CI, 1.03–1.77) and by over 2-fold in Department B (OR = 2.09; 95{\%} CI, 1.19–3.66). In Department B, hard cornering (OR = 1.14; 95{\%} CI, 1.03–1.26) and emergency speeding (OR = 1.65; 95{\%} CI, 1.06–2.57) were also associated with increased odds of crash. The final LASSO risk index model had a sensitivity of 73{\%} and specificity of 57{\%}. Conclusions: Harsh braking and excessive speeding were driving behaviors most associated with crash in the fire service. Telematics may be a useful tool for monitoring driver safety in the fire service.",
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AU - Bui, D. P.

AU - Hu, Chengcheng

AU - Jung, A. M.

AU - Pollack Porter, K. M.

AU - Griffin, Stephanie

AU - French, D. D.

AU - Crothers, S.

AU - Burgess, Jefferey L

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N2 - Objective: Emergency service vehicle incidents are a leading cause of firefighter fatalities and are also hazardous to civilian road users. Modifiable driving behaviors may be associated with emergency service vehicle incidents. The goal of this study was to use telematics to identify driving behaviors associated with crashes in the fire service. Methods: Forty-three emergency service vehicles in 2 fire departments were equipped with telematics devices (12 in Department A and 31 in Department B). The devices collected vehicle coordinates, speed, and g forces, which were monitored for exceptions to driving rules established by the fire departments regarding speeding, harsh braking, and hard cornering. Fire department administrative reports were used to identify vehicles involved in crashes and merged with daily telematics data. Penalized logistic regression was used to identify driving rules associated with crashes. Least absolute shrinkage and selection operator (LASSO) regression was used to generate a telematics-based risk index for emergency service vehicle incidents. Results: Nearly 1.1 million km of driving data and 44 crashes were recorded among the 2 departments during the study. Harsh braking was associated with increased odds of crash in Department A (odds ratio [OR] = 2.22; 95% confidence interval [CI], 1.09–4.51) and Department B (OR = 1.55; 95% CI, 1.12–2.15). For every kilometer of nonemergency speeding, the odds of crash increased by 35% in Department A (OR = 1.35; 95% CI, 1.03–1.77) and by over 2-fold in Department B (OR = 2.09; 95% CI, 1.19–3.66). In Department B, hard cornering (OR = 1.14; 95% CI, 1.03–1.26) and emergency speeding (OR = 1.65; 95% CI, 1.06–2.57) were also associated with increased odds of crash. The final LASSO risk index model had a sensitivity of 73% and specificity of 57%. Conclusions: Harsh braking and excessive speeding were driving behaviors most associated with crash in the fire service. Telematics may be a useful tool for monitoring driver safety in the fire service.

AB - Objective: Emergency service vehicle incidents are a leading cause of firefighter fatalities and are also hazardous to civilian road users. Modifiable driving behaviors may be associated with emergency service vehicle incidents. The goal of this study was to use telematics to identify driving behaviors associated with crashes in the fire service. Methods: Forty-three emergency service vehicles in 2 fire departments were equipped with telematics devices (12 in Department A and 31 in Department B). The devices collected vehicle coordinates, speed, and g forces, which were monitored for exceptions to driving rules established by the fire departments regarding speeding, harsh braking, and hard cornering. Fire department administrative reports were used to identify vehicles involved in crashes and merged with daily telematics data. Penalized logistic regression was used to identify driving rules associated with crashes. Least absolute shrinkage and selection operator (LASSO) regression was used to generate a telematics-based risk index for emergency service vehicle incidents. Results: Nearly 1.1 million km of driving data and 44 crashes were recorded among the 2 departments during the study. Harsh braking was associated with increased odds of crash in Department A (odds ratio [OR] = 2.22; 95% confidence interval [CI], 1.09–4.51) and Department B (OR = 1.55; 95% CI, 1.12–2.15). For every kilometer of nonemergency speeding, the odds of crash increased by 35% in Department A (OR = 1.35; 95% CI, 1.03–1.77) and by over 2-fold in Department B (OR = 2.09; 95% CI, 1.19–3.66). In Department B, hard cornering (OR = 1.14; 95% CI, 1.03–1.26) and emergency speeding (OR = 1.65; 95% CI, 1.06–2.57) were also associated with increased odds of crash. The final LASSO risk index model had a sensitivity of 73% and specificity of 57%. Conclusions: Harsh braking and excessive speeding were driving behaviors most associated with crash in the fire service. Telematics may be a useful tool for monitoring driver safety in the fire service.

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KW - driving behaviors

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KW - firefighters

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