Design of the In-Vehicle Driving Behavior and Crash Risk Study: In Support of the SHRP 2 Naturalistic Driving Study

2011 ◽  
Author(s):  
Jon Antin ◽  
Suzie Lee ◽  
Jon Hankey ◽  
Tom Dingus ◽  
◽  
...  
Keyword(s):  
Driving Behavior ◽  
Crash Risk ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

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.

Driver speed selection and crash risk: Insights from the naturalistic driving study

2017 ◽  
Vol 63 ◽  
pp. 187-194 ◽  
Author(s):  
Raha Hamzeie ◽  
Peter T. Savolainen ◽  
Timothy J. Gates
Keyword(s):  
Crash Risk ◽  
Naturalistic Driving Study ◽  
Speed Selection ◽  
Naturalistic Driving

Crash and Near-Crash Risk Assessment of Distracted Driving and Engagement in Secondary Tasks: A Naturalistic Driving Study

2018 ◽  
Vol 2672 (38) ◽  
pp. 245-254 ◽  
Author(s):  
Peter R. Bakhit ◽  
BeiBei Guo ◽  
Sherif Ishak
Keyword(s):  
Traffic Safety ◽  
Real World ◽  
Secondary Task ◽  
Safety Concern ◽  
Distracted Driving ◽  
Crash Risk ◽  
Secondary Tasks ◽  
Depth Analysis ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Distracted driving behavior is a perennial safety concern that affects not only the vehicle’s occupants but other road users as well. Distraction is typically caused by engagement in secondary tasks and activities such as manipulating objects and passenger interaction, among many others. This study provides an in-depth analysis of the increased crash/near-crash risk associated with different secondary tasks using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data-mining techniques were developed to analyze the distracted driving and crash risk. First, a bivariate probit model was constructed to investigate the relationship between engagement in a secondary task and the safety-critical events likelihood. Subsequently, two different techniques were implemented to quantify the increased crash/near-crash risk because of involvement in a particular secondary task. The first technique used the baseline-category logits model to estimate the increased crash risk in terms of conditional odds ratios. The second technique used the a priori association rule mining algorithm to reveal the risk associated with each secondary task in terms of support, confidence, and lift indexes. The results indicate that reaching for objects, manipulating objects, reading, and cell phone texting are the highest crash risk factors among various secondary tasks. Recognizing the effect of different secondary tasks on traffic safety in a real-world environment helps legislators enact laws that reduce crashes resulting from distracted driving, as well as enabling government officials to make informed decisions about the allocation of available resources to reduce roadway crashes and improve traffic safety.

Longitudinal Risky Driving Behavior Recognition Based on Naturalistic Driving Study

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Zhengwei Sun ◽  
Xin Pei ◽  
Pengju Wang ◽  
Haowei Liu ◽  
Dajun Wang ◽  
...  
Keyword(s):  
Driving Behavior ◽  
Behavior Recognition ◽  
Risky Driving ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Accounting for Driver Distraction and Socioeconomic Characteristics in a Crash Risk Index: Naturalistic Driving Study

2017 ◽  
Vol 2659 (1) ◽  
pp. 204-211 ◽  
Author(s):  
Mengqiu Ye ◽  
Osama A. Osman ◽  
Sherif Ishak
Keyword(s):  
Risk Index ◽  
The United States ◽  
Distracted Driving ◽  
Crash Risk ◽  
Grading System ◽  
Secondary Tasks ◽  
Auto Insurance ◽  
Socioeconomic Characteristics ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Distracted driving has long been acknowledged as one of the main contributors to crashes in the United States. According to past studies, driving behavior proved to be influenced by the socioeconomic characteristics of drivers. However, few studies attempted to quantify that influence. This study proposed a crash risk index (CRI) to estimate the crash risk associated with the socioeconomic characteristics of drivers and their tendency to experience distracted driving. The analysis was conducted with data from the SHRP 2 Naturalistic Driving Study. The proposed CRI was developed on a grading system of three measures: the crash risk associated with performing secondary tasks during driving, the effect of socioeconomic attributes (e.g., age) on the likelihood of engagement in secondary tasks, and the effect of specific categories within each socioeconomic attribute (e.g., age older than 60) on the likelihood of engagement in secondary tasks. Logistic regression analysis was performed on the secondary tasks, socioeconomic attributes, and specific socioeconomic characteristics. The results identified the significant secondary tasks with high crash risk and the socioeconomic characteristics with significant effect on determining drivers’ involvement in secondary tasks in each tested parameter. These results were used to quantify the grading system measures and hence estimate the proposed CRI. This index indicates the relative crash risk associated with the socioeconomic characteristics of drivers and considers the possibility of engagement in secondary tasks. The proposed CRI and the associated grading system are plausible methods for estimating auto insurance premiums.

scholarly journals Vehicle ownership and other predictors of teenagers risky driving behavior: Evidence from a naturalistic driving study

2018 ◽  
Vol 118 ◽  
pp. 96-101 ◽  
Author(s):  
Pnina Gershon ◽  
Johnathon Ehsani ◽  
Chunming Zhu ◽  
Fearghal O’Brien ◽  
Sheila Klauer ◽  
...  
Keyword(s):  
Driving Behavior ◽  
Vehicle Ownership ◽  
Risky Driving ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

scholarly journals Evaluation of driving behavior on rural 2-lane curves using the SHRP 2 naturalistic driving study data

2015 ◽  
Vol 54 ◽  
pp. 17.e1-27 ◽  
Author(s):  
Shauna L. Hallmark ◽  
Samantha Tyner ◽  
Nicole Oneyear ◽  
Cher Carney ◽  
Dan McGehee
Keyword(s):  
Driving Behavior ◽  
Study Data ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving
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