scholarly journals Adjusted Crash Odds Ratio Estimates of Driver Behavior Errors: A Re-Analysis of the SHRP2 Naturalistic Driving Study Data

2017 ◽  
Author(s):  
Richard A Young
Keyword(s):  
Odds Ratio ◽  
Driver Behavior ◽  
Study Data ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Using Naturalistic Driving Study Data to Evaluate the Effects of Intersection Balance on Driver Behavior at Partial Cloverleaf Interchange Terminals

2018 ◽  
Vol 2672 (38) ◽  
pp. 255-265
Author(s):  
Jin Wang ◽  
Huaguo Zhou
Keyword(s):  
Traffic Safety ◽  
Driver Behavior ◽  
Study Data ◽  
Statistical Characteristics ◽  
Proof Of Concept ◽  
Left Turn ◽  
Sight Distance ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Entrance Ramps

Past studies showed that poor intersection balances at partial cloverleaf (parclo) interchange terminals significantly impact traffic safety and sight distance of drivers making left turns to entrance ramps. Some state traffic agencies have recommended a “balance” guideline that the length between the left-turn stop line on crossroads to the middle of the intersection should not be greater than 60% of the entire length of the intersection. However, a scarcity of research exists on how the balance of an intersection affects driver behavior, which has been identified as a critical contributing factor to intersection-related crashes. This study utilizes the Naturalistic Driving Study (NDS) data to evaluate the effects of intersection balance on driver behavior at parclo interchange terminals for proof-of-concept. A small but representative data sample was collected from the second Strategic Highway Research Program’s (SHRP 2) NDS dataset. It demonstrates statistical characteristics and overall trends of driver speed, acceleration/deceleration rates, and risk perception with the changing of intersection balances. Conclusions provide guidance on optimal intersection balance design that may help drivers make smoother and safer transitions from crossroads to entrance ramps at parclo interchange terminals.

Using Naturalistic Driving Study Data to Investigate Driver Behavior at Highway-Rail Grade Crossings

2018 ◽  
Author(s):  
Alawudin Salim ◽  
Myounghoon Jeon ◽  
Pasi Lautala ◽  
David Nelson
Keyword(s):  
Driver Behavior ◽  
Study Data ◽  
Future Research ◽  
Loss Of Life ◽  
Speed Reduction ◽  
Early Results ◽  
Rail Industry ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Grade Crossings

Although accidents at Highway-Rail Grade Crossings (HRGCs) have been greatly reduced over the past decades, they continue to be a major problem for the rail industry, causing injuries, loss of life, and loss of revenue. Recently, the Strategic Highway Research Program sponsored a Naturalistic Driving Study, the SHRP2 NDS, which produced a unique opportunity to look at how drivers behave while traversing HRGCs. This research deviates from previous studies by concentrating on day-to-day actions of drivers who traverse the HRGCs without an incident, instead of focusing on the accident events that have formed the foundation most earlier studies. This paper will focus on the effects of the external environment, weather and day/night conditions, on driver behavior at HRGCs. We will present the methodology and data used for the study and provide some early results from the analysis, such as differences in compliance during poor versus clear weather. We will use both a compliance score based on scanning and speed reduction and an analysis of brake and gas pedal usage during the approach to a HRGC. The paper will conclude with a brief discussion of future research concepts.

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.

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