Exploring the combined effects of driving situations on freeway rear-end crash risk using naturalistic driving study data

Even though drivers disregarding a stop sign is widely considered a major contributing factor for crashes at unsignalized intersections, an equally important problem that leads to severe crashes at such locations is misjudgment of gaps. This paper presents the results of an effort to fully understand gap acceptance behavior at unsignalized intersections using SHPR2 Naturalistic Driving Study data. The paper focuses on the findings of two research activities: the identification of critical gaps for common traffic/roadway scenarios at unsignalized intersections, and the investigation of significant factors affecting driver gap acceptance behaviors at such intersections. The study used multiple statistical and machine learning methods, allowing a comprehensive understanding of gap acceptance behavior while demonstrating the advantages of each method. Overall, the study showed an average critical gap of 5.25 s for right-turn and 6.19 s for left-turn movements. Although a variety of factors affected gap acceptance behaviors, gap size, wait time, major-road traffic volume, and how frequently the driver drives annually were examples of the most significant.

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Studying Driving Behavior on Horizontal Curves using Naturalistic Driving Study Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118784384 ◽

2018 ◽

Vol 2672 (17) ◽

pp. 83-95 ◽

Cited By ~ 5

Author(s):

Bashar Dhahir ◽

Yasser Hassan

Keyword(s):

Data Collection ◽

Weather Conditions ◽

Driving Behavior ◽

Study Data ◽

Output Analysis ◽

Horizontal Curves ◽

Naturalistic Driving Study ◽

Naturalistic Driving ◽

Design Consistency ◽

Favorable Weather

Many studies have been conducted to develop models to predict speed and driver comfort thresholds on horizontal curves, and to evaluate design consistency. The approaches used to develop these models differ from one another in data collection, data processing, assumptions, and analysis. However, some issues might be associated with the data collection that can affect the reliability of collected data and developed models. In addition, analysis of speed behavior on the assumption that vehicles traverse horizontal curves at a constant speed is far from actual driving behavior. Using the Naturalistic Driving Study (NDS) database can help overcome problems associated with data collection. This paper aimed at using NDS data to investigate driving behavior on horizontal curves in terms of speed, longitudinal acceleration, and comfort threshold. The NDS data were valuable in providing clear insight on drivers’ behavior during daytime and favorable weather conditions. A methodology was developed to evaluate driver behavior and was coded in Matlab. Sensitivity analysis was performed to recommend values for the parameters that can affect the output. Analysis of the drivers’ speed behavior and comfort threshold highlighted several issues that describe how drivers traverse horizontal curves that need to be considered in horizontal curve design and consistency evaluation.

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Crash Factor Analysis in Intersection-Related Crashes Using SHRP 2 Naturalistic Driving Study Data

10.4271/2021-01-0872 ◽

2021 ◽

Author(s):

Bingrui Jia ◽

Dennis Guenther ◽

Gary Heydinger

Keyword(s):

Factor Analysis ◽

Study Data ◽

Naturalistic Driving Study ◽

Naturalistic Driving

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Cell Phone Conversation and Relative Crash Risk Update

Encyclopedia of Information Science and Technology, Fourth Edition ◽

10.4018/978-1-5225-2255-3.ch521 ◽

2018 ◽

pp. 5992-6006 ◽

Cited By ~ 1

Author(s):

Richard A. Young

Keyword(s):

Protective Effect ◽

Cell Phone ◽

Response Times ◽

Meta Analysis ◽

Self Regulation ◽

Study Data ◽

Crash Risk ◽

Recent Experimental Study ◽

A Cell ◽

Naturalistic Driving

This chapter reviews key findings since 2014 that are relevant to estimating the relative crash risk of conversing via a cell phone during real-world and naturalistic driving in passenger vehicles. It updates chapter 102 in the previous edition of this Encyclopedia (Young, 2015a). The objective is to determine if recent data confirms the conclusion in Young (2015a) that engaging in a cell phone conversation does not increase crash risk beyond that of driving without engaging in a cell phone conversation. In particular, a recent estimate is presented of the relative crash risk for cell phone conversation in the Strategic Highway Research Program 2 (SHRP2) naturalistic driving study data. This estimate is compared with five other estimates in a meta-analysis, which shows that cell phone conversation reduces crash risk (i.e., has a protective effect). A recent experimental study will also be discussed, which supports the hypothesis that driver self-regulation gives rise to the protective effect by compensating for the slight delays in event response times during cell phone conversation.

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Risk Factors in Work Zone Safety Events: A Naturalistic Driving Study Analysis

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118821630 ◽

2019 ◽

Vol 2673 (1) ◽

pp. 379-387 ◽

Cited By ~ 3

Author(s):

Nipjyoti Bharadwaj ◽

Praveen Edara ◽

Carlos Sun

Keyword(s):

Risk Factors ◽

Driver Behavior ◽

Work Zone ◽

Crash Risk ◽

Work Zones ◽

Binary Logistic Regression Model ◽

Work Zone Safety ◽

Naturalistic Driving Study ◽

Naturalistic Driving ◽

The Impact

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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