Using naturalistic driving study data to investigate the impact of driver distraction on driver's brake reaction time in freeway rear-end events in car-following situation

2017 ◽  
Vol 63 ◽  
pp. 195-204 ◽  
Author(s):  
Jingru Gao ◽  
Gary A. Davis
Keyword(s):  
Reaction Time ◽  
Study Data ◽  
Driver Distraction ◽  
Car Following ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
The Impact

Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110071
Author(s):  
Yingfeng (Eric) Li ◽  
Haiyan Hao ◽  
Ronald B. Gibbons ◽  
Alejandra Medina
Keyword(s):  
Road Traffic ◽  
Wait Time ◽  
Study Data ◽  
Gap Acceptance ◽  
Factors Affecting ◽  
Unsignalized Intersections ◽  
Research Activities ◽  
Stop Sign ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

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.

Studying Driving Behavior on Horizontal Curves using Naturalistic Driving Study Data

2018 ◽  
Vol 2672 (17) ◽  
pp. 83-95 ◽  
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.

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

2019 ◽  
Vol 2673 (1) ◽  
pp. 379-387 ◽  
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.

scholarly journals A preliminary investigation of the effectiveness of high visibility enforcement programs using naturalistic driving study data: A grouped random parameters approach

2019 ◽  
Vol 21 ◽  
pp. 1-12 ◽  
Author(s):  
Sarvani Sonduru Pantangi ◽  
Grigorios Fountas ◽  
Md Tawfiq Sarwar ◽  
Panagiotis Ch. Anastasopoulos ◽  
Alan Blatt ◽  
...  
Keyword(s):  
Preliminary Investigation ◽  
Study Data ◽  
Random Parameters ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving
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