Youjia Fang

Individual driver risk assessment using naturalistic driving data

Driving risk varies substantially among drivers. Identifying and predicting high-risk drivers will greatly benefit the development of proactive driver education programs and safety countermeasures. The objective of this study is twofold: (1) to identify factors associated with individual driver risk and (2) predict high-risk drivers using demographic, personality, and driving characteristic data. The 100-Car Naturalistic Driving Study was used for methodology development and application. A negative binomial regression model was adopted to identify significant risk factors. The results indicated that the drivers age, personality, and critical incident rate had significant impacts on crash and near-crash risk. For the second objective, drivers were classified into three risk groups based on crash and near-crash rate using a K-mean cluster method. The cluster analysis identified approximately 6% of drivers as high-risk drivers, with average crash and near-crash (CNC) rate of 3.95 per 1000miles traveled, 12% of drivers as moderate-risk drivers (average CNC rate=1.75), and 84% of drivers as low-risk drivers (average CNC rate=0.39). Two logistic models were developed to predict the high- and moderate-risk drivers. Both models showed high predictive powers with area under the curve values of 0.938 and 0.930 for the receiver operating characteristic curves. This study concluded that crash and near-crash risk for individual drivers is associated with critical incident rate, demographic, and personality characteristics. Furthermore, the critical incident rate is an effective predictor for high-risk drivers.

The effects of age on crash risk associated with driver distraction

Background Driver distraction is a major contributing factor to crashes, which are the leading cause of death for the US population under 35 years of age. The prevalence of secondary-task engagement and its impacts on distraction and crashes may vary substantially by driver age. Methods Driving performance and behaviour data were collected continuously using multiple cameras and sensors in situ for 3542 participant drivers recruited for up to 3 years for the Second Strategic Highway Research Program Naturalistic Driving Study. Secondary-task engagement at the onset of crashes and during normal driving segments was identified from videos. A case-cohort approach was used to estimate the crash odds ratios associated with, and the prevalence of, secondary tasks for four age groups: 16-20, 21-29, 30-64 and 65-98 years of age. Only severe crashes (property damage and higher severity) were included in the analysis. Results Secondary-task-induced distraction posed a consistently higher threat for drivers younger than 30 and above 65 when compared with middle-aged drivers, although senior drivers engaged in secondary tasks much less frequently than their younger counterparts. Secondary tasks with high visual-manual demand (e.g. visual-manual tasks performed on cell phones) affected drivers of all ages. Certain secondary tasks, such as operation of in-vehicle devices and talking/singing, increased the risk for only certain age groups. Conclusions Teenaged, young adult drivers and senior drivers are more adversely impacted by secondary-task engagement than middle-aged drivers. Visual-manual distractions impact drivers of all ages, whereas cognitive distraction may have a larger impact on young drivers.

The influence of daily sleep patterns of commercial truck drivers on driving performance.

Fatigued and drowsy driving has been found to be a major cause of truck crashes. Lack of sleep is the number one cause of fatigue and drowsiness. However, there are limited data on the sleep patterns (sleep duration, sleep percentage in the duration of non-work period, and the time when sleep occurred) of truck drivers in non-work periods and the impact on driving performance. This paper examined sleep patterns of 96 commercial truck drivers during non-work periods and evaluated the influence these sleep patterns had on truck driving performance. Data were from the Naturalistic Truck Driving Study. Each driver participated in the study for approximately four weeks. A shift was defined as a non-work period followed by a work period. A total of 1397 shifts were identified. Four distinct sleep patterns were identified based on sleep duration, sleep start/end point in a non-work period, and the percentage of sleep with reference to the duration of non-work period. Driving performance was measured by safety-critical events, which included crashes, near-crashes, crash-relevant conflicts, and unintentional lane deviations. Negative binomial regression was used to evaluate the association between the sleep patterns and driving performance, adjusted for driver demographic information. The results showed that the sleep pattern with the highest safety-critical event rate was associated with shorter sleep, sleep in the early stage of a non-work period, and less sleep between 1 a.m. and 5 a.m. This study also found that male drivers, with fewer years of commercial vehicle driving experience and higher body mass index, were associated with deteriorated driving performance and increased driving risk. The results of this study could inform hours-of-service policy-making and benefit safety management in the trucking industry.

A validation of the low mileage bias using naturalistic driving study data

INTRODUCTION This paper evaluated the low mileage bias (LMB) phenomenon for senior drivers using data mined from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study. Supporters of the LMB construct postulate that it is only those seniors who drive the lowest annual mileage who are primarily responsible for the increased crash rates traditionally attributed to this population in general. METHOD The current analysis included 802 participants, all aged 65 or older who were involved in 163 property damage and injury crashes, and deemed to be at-fault in 123 (75%) of those instances. Poisson regression models were used to evaluate the association between annualized mileage driven and crash risk. RESULTS Results show that the crash rate for drivers with lower annualized mileage (i.e., especially for those driving fewer than approximately 3000miles per year) was significantly higher than that of drivers with higher annualized mileage, and that 25% of the overall sample were low- mileage drivers according to this criterion. Data were also evaluated by gender and meta-age group (i.e., younger-old: 65-74 and older-old: 75-99), and the results were consistent across these sub-groups. CONCLUSIONS This study provides strong support for the existence of the LMB. PRACTICAL APPLICATIONS These results can help to reshape how transportation safety stakeholders view senior drivers in general and help them to focus their efforts on those seniors most in need of either risk-reducing countermeasures or alternative means of transportation.

Investigate moped-car conflicts in China using a naturalistic driving study approach

PROBLEM Mopeds are a popular transportation mode in Europe and Asia. Moped-related traffic accidents account for a large proportion of crash fatalities. To develop moped-related crash countermeasures, it is important to understand the characteristics of moped-related conflicts. METHOD Naturalistic driving study data were collected in Shanghai, China from 36 car drivers. The data included 2,878h and 78,296km driven from 13,149 trips. Moped-car conflicts were identified and examined from the passenger car drivers perspective using kinematic trigger algorithms and manual video reduction. RESULTS A total of 119 moped-car conflicts were identified, including 74 high g-force conflicts and 45 low g-force events. These conflicts were classified into 22 on-road configurations where both similarities and differences were found as compared to Western Countries. The majority of the conflicts occurred on secondary main roads and branch roads. Hard braking was the primary response that the car drivers made to these conflicts rather than hard steering. DISCUSSIONS The identified on-road vehicle-moped conflict configurations in Shanghai, China may be attributed to the complicated traffic environment and risky behavior of moped riders. The lower prevalence of hard steering in Shanghai as compared to the United States may be due to the lower speeds at event onsets or less available steering space, e.g., less available shoulder area on Chinese urban roads. CONCLUSIONS The characteristics of moped-car conflicts may impact the design of active safety countermeasures on passenger cars. The pilot data from Shanghai urban areas suggest that countermeasures developed for China may require some modifications to those developed for the United States and European countries, although this recommendation may not be conclusive given the small sample size of the study. Future studies with large samples may help better understand the characteristics of moped-car conflicts.