Effectiveness of Using SHRP2 Naturalistic Driving Study Data to Analyze Driver Behavior at Highway-Rail Grade Crossings

2017 ◽  
Author(s):  
Aaron Dean ◽  
Pasi Lautala ◽  
David Nelson
Keyword(s):  
Machine Vision ◽  
Large Scale ◽  
Driver Behavior ◽  
Test Sample ◽  
Head Tracking ◽  
Tracking Data ◽  
Data Set ◽  
Preliminary Results ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Highway-rail grade crossing (crossing) collisions and fatalities have been in decline, but a recent ‘plateau’ has caused the Federal Railroad Administration (FRA) to concentrate on decreasing further casualties. The Michigan Tech Rail Transportation Program has been selected to perform a large-scale study that will utilize the SHRP2 Naturalistic Driving Study (NDS) data to analyze how various crossing warning devices affect driver behavior and whether there are clear differences between the effectiveness of the warning devices. The main results of this study are the development of a coding scheme for a visual narrative, used to validate machine vision head tracking data, and an improved baseline for the head tracking data using bivariate probability density. Head tracking data from the NDS and its correlation with coded narratives are vital to analyze driver behavior as they traverse crossings. This paper also presents preliminary results for the comparative analysis of the head tracking data from an initial test sample. Future work will extend the analysis to a larger data set, and ensure that use of the head tracking data is a viable tool for the ongoing behavior analysis work. Based on preliminary results from testing of the first data set, it is expected there will be significant positive correlation in future samples and the machine vision head tracking will prove consistent enough for use in the large scale behavioral study.

Naturalistic Data Collection of Driver Performance in Familiar and Unfamiliar Vehicles

2005 ◽  
Vol 49 (22) ◽  
pp. 1994-1998
Author(s):  
Suzanne E. Lee ◽  
Thomas A. Dingus ◽  
Sheila G. Klauer ◽  
Vicki L. Neale ◽  
Jeremy Sudweeks
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Data Set ◽  
Driver Performance ◽  
Final Data ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Instrumented Vehicles ◽  
Instrumented Vehicle ◽  
Privately Owned

The 100-Car Naturalistic Driving Study was the first large-scale instrumented vehicle study with no special driver instructions, unobtrusive data collection instrumentation, and no in-vehicle experimenter. The final data set includes approximately 2,000,000 vehicle miles, almost 43,000 hours of data, 241 primary and secondary drivers, 12 to 13 months of data collection for each vehicle, and data from a highly capable instrumentation system. In addition, 78 of 102 vehicles were privately owned and 22 were leased. After 12 months, leased vehicles were provided to 22 private vehicle drivers who then drove the leased vehicles for an additional four weeks. Driving performance for the same drivers in familiar and unfamiliar instrumented vehicles was then compared. Results provided evidence of increased relative risk for the same driver for weeks 1 through 4 of driving an unfamiliar leased vehicle as compared to the same period of driving their privately owned vehicle.

Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets

2012 ◽  
Vol 13 (2) ◽  
pp. 553-564 ◽  
Author(s):  
Christer Ahlstrom ◽  
Trent Victor ◽  
Claudia Wege ◽  
Erik Steinmetz
Keyword(s):  
Large Scale ◽  
Data Sets ◽  
Head Tracking ◽  
Tracking Data ◽  
Naturalistic Driving

scholarly journals MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction With Automation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 102021-102038 ◽  
Author(s):  
Lex Fridman ◽  
Daniel E. Brown ◽  
Michael Glazer ◽  
William Angell ◽  
Spencer Dodd ◽  
...  
Keyword(s):  
Large Scale ◽  
Driver Behavior ◽  
Technology Study ◽  
Naturalistic Driving Study ◽  
Vehicle Technology ◽  
Naturalistic Driving

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.

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.

Investigating Effect of Driver-, Vehicle-, and Road-Related Factors on Location-Specific Crashes with Naturalistic Driving Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 46-56 ◽  
Author(s):  
Grace Ashley ◽  
Osama A. Osman ◽  
Sherif Ishak ◽  
Julius Codjoe
Keyword(s):  
United States ◽  
Machine Learning ◽  
Traffic Accidents ◽  
The United States ◽  
Crash Risk ◽  
Related Factors ◽  
Data Set ◽  
Intersection Safety ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

According to NHTSA, traffic accidents cost the United States billions of U.S. dollars each year. Intersection accidents alone accounted for 23% of the 32,675 motor crash deaths in 2014. With the advent of the largest naturalistic driving data set in the United States collected by the SHRP2 Naturalistic Driving Study project, this study performs a crash-only analysis to identify driver-, vehicle-, and roadway-related factors that affect the driving risk at different location types using a machine learning tool. The study then analyzes the most important factors obtained from the machine learning analysis to identify how they affect crash risk. The results, in order of importance of variables, were driver behavior, locality, lane occupied, alignment, and through travel lanes. Also, drivers who violated traffic signals were four times more likely to be involved in a crash than drivers who did not. Those who violated stop signs were two times more likely to be involved in crashes than those who did not. Drivers performing visual-manual (VM) tasks at uncontrolled intersections were 2.7 times more likely to be involved in crashes than those who did not engage in these tasks. At nonintersections, drivers who performed VM tasks were 3.4 times more likely to be involved in crashes than drivers who did not. These findings add to the evidence that the establishment of safety awareness programs geared toward intersection safety is imperative.

Using Naturalistic Driving Data to Develop a Typology of Speeding Episodes

2017 ◽  
Vol 2659 (1) ◽  
pp. 91-97 ◽  
Author(s):  
Christian M. Richard ◽  
James L. Brown ◽  
Randolph Atkins ◽  
Gautam Divekar
Keyword(s):  
United States ◽  
The United States ◽  
Speed Limit ◽  
Data Set ◽  
Slowing Down ◽  
Situational Characteristics ◽  
Duration Magnitude ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Study Participants

Speeding-related crashes continue to be a serious problem in the United States. A recently completed NHTSA project, Motivations for Speeding, collected data to address questions about driver speeding behavior. This naturalistic driving study used 1-Hz GPS units to collect data from 88 drivers in Seattle, Washington, to record how fast vehicles traveled on different roadways. The current project further developed this data set to redefine speeding in terms of speeding episodes, which were continuous periods in which drivers exceeded the posted speed limit by at least 10 mph. More than half of all study participants averaged less than one speeding episode per trip taken. Various characteristics of speeding episodes representing aspects such as duration, magnitude, variability, and overall form of speeding were examined. Cluster analyses conducted using these characteristics of speeding episodes identified six types of speeding. These included two types of speeding that occurred around speed-zone transitions (speeding up and slowing down), incidental speeding, casual speeding, cruising speeding, and aggressive speeding. Qualitative examination of the speeding types indicated that these types also differed in terms of the prevalence of additional risky situational characteristics.

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