Using naturalistic driving data to explore the association between traffic safety-related events and crash risk at driver level

Wu, Kun-Feng; Agüero-Valverde, Jonathan; Jovanis, Paul P.

doi:10.1016/j.aap.2014.07.005

Cited by 80 publications

(49 citation statements)

References 39 publications

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“…Therefore, previous studies presented different definitions for describing near-crashes. Wu et al [13] focused only on braking events and identified a near-crash event where braking is the primary evasive action. Wang et al [17] defined a near-crash by reaching a threshold value of vehicle acceleration (longitudinal: −1.5 m/s 2 ; lateral: −1 m/s 2 ).…”

Section: The Definition Of a Near-crash Eventmentioning

confidence: 99%

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Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model

et al. 2018

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Abstract:With the considerable increase in ownership of motor vehicles, traffic crashes have become a challenge. This paper presents a study of naturalistic driving conducted to collect driving data. The experiments were performed on different road types in the city of Wuhan in China. The collected driving data were used to develop a near-crash database, which covers driving behavior, near-crash factors, driving environment, time, demographics, and experience. A new definition of near-crash events is also proposed. The new definition considers potential risks in driving behavior, such as braking pressure, time headway, and deceleration. A clustering analysis was carried out through a K-means algorithm to classify near-crash events based on their risk level. In addition, a mixed-ordered logit model was used to examine the contributing factors associated with the driving risk of near-crash events. The results indicate that ten factors significantly affect the driving risk of near-crash events: deceleration average, vehicle kinetic energy, near-crash causes, congestion on roads, time of day, driving miles, road types, weekend, age, and experience. The findings may be used by transportation planners to understand the factors that influence driving risk and may provide valuable insights and helpful suggestions for improving transportation rules and reducing traffic collisions thus making roads safer.

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Section: The Definition Of a Near-crash Eventmentioning

confidence: 99%

“…To examine the factors associated with a high risk of near-crashes, this study followed approaches from previous studies for collecting and obtaining driving data variables [13][14][15]. This study introduces a comprehensive database, which includes the majority of factors that may influence the risk of near-crashes.…”

Section: Data Preparationmentioning

confidence: 99%

Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model

et al. 2018

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“…Most of the published safety-related research on IVMS has been conducted in non-occupational settings using volunteers (both teenage and adult) from the general population, both in real-world driving conditions and driving simulators (Carney, McGehee, Lee, Reyes, & Raby, 2010; Donmez, Boyle, & Lee, 2007, 2008; Farmer, Kirkey, & McCartt, 2010; McGehee, Raby, Carney, Lee, & Reyes, 2007; Merrikhpour et al, 2014; Roberts, Horrey, & Liang, 2016; Simons-Morton et al, 2013; Wu, Ageuro-Valverde, & Jovanis, 2014). Research on effectiveness of IVMS for improving driving behaviors of workers during work time is less extensive, and has focused on short-haul and long-haul truck drivers (Hickman & Geller, 2003; Hickman & Hanowski, 2011; Lisk, Cruice, & Pollard, 2013), technicians (unspecified industry) driving to make service calls (Toledo, Musicant, & Lotan, 2008), and emergency medical services (EMS) drivers (Levick & Swanson, 2005).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an in-vehicle monitoring system (IVMS) to reduce risky driving behaviors in commercial drivers: Comparison of in-cab warning lights and supervisory coaching with videos of driving behavior

Bell

Taylor

Chen

et al. 2017

Journal of Safety Research

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Problem Roadway incidents are the leading cause of work-related death in the United States. Methods The objective of this research was to evaluate whether two types of feedback from a commercially available in-vehicle monitoring system (IVMS) would reduce the incidence of risky driving behaviors in drivers from two companies. IVMS were installed in 315 vehicles representing the industries of local truck transportation and oil and gas support operations, and data were collected over an approximate two-year period in intervention and control groups. In one period, intervention group drivers were given feedback from in-cab warning lights from an IVMS that indicated occurrence of harsh vehicle maneuvers. In another period, intervention group drivers viewed video recordings of their risky driving behaviors with supervisors, and were coached by supervisors on safe driving practices. Results Risky driving behaviors declined significantly more during the period with coaching plus instant feedback with lights in comparison to the period with lights-only feedback (ORadj = 0.61 95% CI 0.43–0.86; Holm-adjusted p = 0.035) and the control group (ORadj = 0.52 95% CI 0.33–0.82; Holm-adjusted p = 0.032). Lights-only feedback was not found to be significantly different than the control group's decline from baseline (ORadj = 0.86 95% CI 0.51–1.43; Holm-adjusted p > 0.05). Conclusions The largest decline in the rate of risky driving behaviors occurred when feedback included both supervisory coaching and lights. Practical applications Supervisory coaching is an effective form of feedback to improve driving habits in the workplace. The potential advantages and limitations of this IVMS-based intervention program are discussed.

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“…Naturalistic Driving Studies (NDS) were conducted in many countries all over the world (Dingus et al, 2006) using EDR (Event Data Recorder) and video camera to collect large-scale data, among which were identified numerous critical situations, including crashes, near-crashes and incidents. Wu et al (2014) identified significantly positive correlations between crashes, near crashes, and crash-relevant incidents, suggesting that drivers involved in more safety-related events are more likely to be involved in a crash. A positive correlation between individual risk of the driver and his preceding crashes had also been established (Guo et al, 2010).…”

Section: Introductionmentioning

confidence: 96%

Acquisition and analysis of road incidents based on vehicle dynamics

Naude¹,

Serre²,

Dubois-Lounis³

et al. 2019

Accident Analysis & Prevention

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Because motor vehicle crashes have decreased during the last decade in many countries in the world and are more diffuse, local authorities have difficulties to define road safety policies. An experiment with 51 cars of public fleets equipped with a specific Event Data Recorder was carried out in France during one year. The purposes of this research were to evaluate if incident data (critical driving situations) help to understand crashes, and to explore a new way for road infrastructure safety diagnosis. The analysis of 339 genuine incidents and 1237 simple events recorded illustrates the potentiality of such an experiment and provides: some insights about conditions in which incidents occur, a general overview of their distribution according to different road layouts, as well as information on the different levels of accelerations reached. It can be noticed that there is an overrepresentation of incidents in right curves compared to left curves. The simple events involving mostly the infrastructure could be used to detect road defects. Genuine incidents where the vehicle is subjected to important dynamic demands, related to potentially unsafe driving situations, can be used to improve knowledge of the motor vehicle crashes thanks to incident mechanisms analysis.

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Using naturalistic driving data to explore the association between traffic safety-related events and crash risk at driver level

Cited by 80 publications

References 39 publications

Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model

Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model

Evaluation of an in-vehicle monitoring system (IVMS) to reduce risky driving behaviors in commercial drivers: Comparison of in-cab warning lights and supervisory coaching with videos of driving behavior

Acquisition and analysis of road incidents based on vehicle dynamics

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