Crash Risk Factors at Rural Two-Way Stop-Controlled Intersections

Identification of crash risk factors and enhancing safety at work zones is a major priority for transportation agencies. There is a critical need for collecting comprehensive data related to work zone safety. The naturalistic driving study (NDS) data offers a rare opportunity for a first-hand view of crashes and near-crashes (CNC) that occur in and around work zones. NDS includes information related to driver behavior and various non-driving related tasks performed while driving. Thus, the impact of driver behavior on crash risk along with infrastructure and traffic variables can be assessed. This study: (1) investigated risk factors associated with safety critical events occurring in a work zone; (2) developed a binary logistic regression model to estimate crash risk in work zones; and (3) quantified risk for different factors using matched case-control design and odds ratios (OR). The predictive ability of the model was evaluated by developing receiver operating characteristic curves for training and validation datasets. The results indicate that performing a non-driving related secondary task for more than 6 seconds increases the CNC risk by 5.46 times. Driver inattention was found to be the most critical behavioral factor contributing to CNC risk with an odds ratio of 29.06. In addition, traffic conditions corresponding to Level of Service (LOS) D exhibited the highest level of CNC risk in work zones. This study represents one of the first efforts to closely examine work zone events in the Transportation Research Board’s second Strategic Highway Research Program (SHRP 2) NDS data to better understand factors contributing to increased crash risk in work zones.

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Contrasting Crash- and Non-Crash-Involved Riders: Analysis of Data from the Motorcycle Crash Causation Study

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119851722 ◽

2019 ◽

Vol 2673 (7) ◽

pp. 122-131 ◽

Cited By ~ 1

Author(s):

Hitesh Chawla ◽

Ilker Karaca ◽

Peter T. Savolainen

Keyword(s):

Risk Factors ◽

Public Health Problem ◽

The United States ◽

Crash Risk ◽

Volume Data ◽

Logistic Regression Models ◽

Significant Public Health Problem ◽

Significant Public Health ◽

Paired Control ◽

Motorcycle Crashes

Motorcycle crashes and fatalities remain a significant public health problem as fatality rates have increased substantially as compared to other vehicle types in the United States. Analysis of causal factors for motorcycle crashes is often challenging given a lack of reliable traffic volume data and the fact that such crashes comprise a relatively small portion of all traffic crashes. Given these limitations, on-scene crash investigations represent an ideal setting through which to investigate the precipitating factors for motorcycle-involved crashes. This study examines motorcycle crash risk factors by employing data recently made available from the Federal Highway Administration Motorcycle Crash Causation Study (MCCS). The MCCS represents a comprehensive investigative effort to determine the causes of motorcycle crashes and involved the collection of in-depth data from 351 crashes, as well as the collection of comparison data from 702 paired control observations in Orange County, California. This dataset provides a unique opportunity to understand how the risk of crash involvement varies across different segments of the riding population. Logistic regression models are estimated to identify the rider and vehicle attributes associated with motorcycle crashes. The results of the study suggest that motorcycle crash risks are related to rider age, physical status, and educational attainment. In addition to such factors outside of the rider’s control, several modifiable risk factors, which arguably affect the riders’ proclivity to take risks, were also found to be significantly associated with motorcycle crash risk, including motorcycle type, helmet coverage, motorcycle ownership, speed, trip destination, and traffic violation history.

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Driving Anger, Aberrant Driving Behaviors, and Road Crash Risk: Testing of a Mediated Model

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16030297 ◽

2019 ◽

Vol 16 (3) ◽

pp. 297 ◽

Cited By ~ 9

Author(s):

Tingru Zhang ◽

Alan H.S. Chan ◽

Hongjun Xue ◽

Xiaoyan Zhang ◽

Da Tao

Keyword(s):

Risk Factors ◽

Road Safety ◽

Road Traffic ◽

Crash Risk ◽

Traffic Crashes ◽

Road Traffic Crashes ◽

Driving Anger ◽

Driving Behaviors ◽

Scale Scores ◽

Road Crash

With the dramatic increase in motorization, road traffic crashes have become the leading cause of death in China. To reduce the losses associated with road safety problems, it is important to understand the risk factors contributing to the high crash rate among Chinese drivers. This study investigated how driving anger and aberrant driving behaviors are related to crash risk by proposing and testing one mediated model. In this model, the effects of driving anger on road crash risk were mediated by aberrant driving behaviors. However, unlike previous studies, instead of using the overall scale scores, the subscales of driving anger and aberrant driving behaviors were used to establish the mediated model in this study. To test the validity of this model, an Internet-based questionnaire, which included various measures of driving anger, aberrant driving, and road crash history, was completed by a sample of 1974 Chinese drivers. The results showed that the model fitted the data very well and aberrant driving behaviors fully mediated the effects of driving anger on road crash risk. Findings from the present study are useful for the development of countermeasures to reduce road traffic crashes in China.

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