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.

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.

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.

Exploration of Tourist Activities in Urban Destination Using Venue Check-In Data

2019 ◽  
Vol 44 (3) ◽  
pp. 472-498
Author(s):  
Huy Quan Vu ◽  
Jian Ming Luo ◽  
Gang Li ◽  
Rob Law
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Traditional Approach ◽  
Urban Tourism ◽  
Data Set ◽  
Tourism Marketing ◽  
Large Scale Data ◽  
New Type ◽  
Scale Data

Understanding the differences and similarities in the activities of tourists from various cultures is important for tourism managers to develop appropriate plans and strategies that could support urban tourism marketing and managements. However, tourism managers still face challenges in obtaining such understanding because the traditional approach of data collection, which relies on survey and questionnaires, is incapable of capturing tourist activities at a large scale. In this article, we present a method for the study of tourist activities based on a new type of data, venue check-ins. The effectiveness of the presented approach is demonstrated through a case study of a major tourism country, France. Analysis based on a large-scale data set from 19 tourism cities in France reveals interesting differences and similarities in the activities of tourists from 14 markets (countries). Valuable insights are provided for various urban tourism applications.

Naturalistic Driving Study: Field Data Collection

2014 ◽  
Author(s):  
Alan Blatt ◽  
John Pierowicz ◽  
Marie Flanigan ◽  
Pei-Sung Lin ◽  
Achilleas Kourtellis ◽  
...  
Keyword(s):  
Data Collection ◽  
Field Data ◽  
Field Data Collection ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Validation of a Novel Smartphone-Based Electronic Data Capture System Following Surgical Fixation of Distal Radial Fractures

Hand ◽  
2022 ◽  
pp. 155894472110573
Author(s):  
Joseph P. Scollan ◽  
Ahmed K. Emara ◽  
Morad Chughtai ◽  
Yuxuan Jin ◽  
Joseph F. Styron
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Distal Radial Fracture ◽  
Completion Rates ◽  
Data Collection Tool ◽  
Perfect Agreement ◽  
Data Set ◽  
Prospective Data ◽  
Electronic Data Capture System ◽  
Operative Notes

Background: Large prospective institutional data provide the opportunity to conduct level II and III studies using robust methodologies and adequately powered sample-sizes, while circumventing limitations of retrospective databases. We aimed to validate a prospective data collection tool, the Orthopaedic Minimal Data Set Episode of Care (OME), implemented at a tertiary North American health care system for distal radial fracture (DRF) open reduction and internal fixation (ORIF). Methods: The first 100 DRF ORIFs performed after OME inception (February 2015) were selected for this validation study. A blinded review of the operative notes and charts was performed, and extracted data of 75 perioperative DRF ORIF procedure variables were compared with OME collected data for agreement. Outcomes included completion rates and agreement measures in OME versus electronic medical record (EMR)-based control datasets. Data counts were evaluated using raw percentages and McNemar tests. Cohen (κ) and concordance correlation coefficient analyzed categorical and numerical variable agreement, respectively. Results: Overall, OME demonstrated superior completion and agreement parameters versus EMR-based retrospective review. Nine data points (12.0%) demonstrated significantly higher completion rates within the OME dataset ( P < .05, each), and 88% (66/75) of captured variables demonstrated similar completion rates. Up to 80.0% (60/75) of variables either demonstrated an agreement proportion of ≥0.90 or were solely reported in the OME. Of 33 variables eligible for agreement analyses, 36.4% (12/33) demonstrated almost perfect agreement (κ > 0.80), and 63.6% (21/33) exhibited almost perfect or substantial agreement (κ > 0.60). Conclusions: The OME is a valid and accurate prospective data collection tool for DRF ORIF that is reliably able to match or supersede traditional retrospective chart review. Future investigations could use this tool for large-scale analyses investigating peri/intraoperative DRF ORIF variables.

scholarly journals EXISTING STRATEGIES FOR ELECTRONIC DATA COLLECTION BY ELDER ABUSE MULTI-DISCIPLINARY TEAMS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S423-S423
Author(s):  
Tony Rosen ◽  
David Burnes ◽  
Darin Kirchin ◽  
Alyssa Elman ◽  
Risa Breckman ◽  
...  
Keyword(s):  
Data Collection ◽  
Social Services ◽  
Large Scale ◽  
Elder Abuse ◽  
The United States ◽  
Third Party ◽  
Snowball Sampling ◽  
Electronic Data ◽  
Data Set ◽  
Electronic Data Collection

Abstract Elder abuse cases often require integrated responses from social services, medicine, civil legal, and criminal justice. Multi-disciplinary teams (MDTs), which meet periodically to discuss and coordinate interventions for complex cases, have developed in many communities. Little is known about how these MDTs collect case-level data. Our objective was to describe existing strategies of case-level electronic data collection conducted by MDTs across the United States as a preliminary step in developing a comprehensive database strategy. To identify MDTs currently collecting data electronically, we used a snowball sampling approach discussing with national leaders. We also sent an e-mail to the National Center for Elder Abuse listserv inviting participation. We identified and reviewed 11 databases from MDTs. Strategies for and comprehensiveness of data collection varied widely. Databases used ranged from a simple spreadsheet to a customized Microsoft Access database to large databases designed and managed by a third-party vendor. Total data fields collected ranged from 12-338. Types of data included intake/baseline case/client information, case tracking/follow-up, and case closure/outcomes. Information tracked by many MDTs, such as type of mistreatment, was not captured in a single standard fashion. Documentation about data entry processes varied from absent to detailed. We concluded that MDTs currently use widely varied strategies to track data electronically and are not capturing data in a standardized fashion. Many MDTs collect only minimal data. Based on this, we have developed recommendations for a minimum data set and optimal data structure. If widely adopted, this would potentially improve ability to conduct large-scale comparative research.

Collecting and evaluating large volumes of bibliographic metadata aggregated in the WorldCat database: a proposed methodology to overcome challenges

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vyacheslav I. Zavalin ◽  
Shawne D. Miksa
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Large Data ◽  
Study Data ◽  
Lessons Learned ◽  
Future Research ◽  
Data Set ◽  
Content Type ◽  
Large Data Set ◽  
Smart Data

Purpose This paper aims to discuss the challenges encountered in collecting, cleaning and analyzing the large data set of bibliographic metadata records in machine-readable cataloging [MARC 21] format. Possible solutions are presented. Design/methodology/approach This mixed method study relied on content analysis and social network analysis. The study examined subject representation in MARC 21 metadata records created in 2020 in WorldCat – the largest international database of “big smart data.” The methodological challenges that were encountered and solutions are examined. Findings In this general review paper with a focus on methodological issues, the discussion of challenges is followed by a discussion of solutions developed and tested as part of this study. Data collection, processing, analysis and visualization are addressed separately. Lessons learned and conclusions related to challenges and solutions for the design of a large-scale study evaluating MARC 21 bibliographic metadata from WorldCat are given. Overall recommendations for the design and implementation of future research are suggested. Originality/value There are no previous publications that address the challenges and solutions of data collection and analysis of WorldCat’s “big smart data” in the form of MARC 21 data. This is the first study to use a large data set to systematically examine MARC 21 library metadata records created after the most recent addition of new fields and subfields to MARC 21 Bibliographic Format standard in 2019 based on resource description and access rules. It is also the first to focus its analyzes on the networks formed by subject terms shared by MARC 21 bibliographic records in a data set extracted from a heterogeneous centralized database WorldCat.

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.

How Dangerous Is Looking Away From the Road? Algorithms Predict Crash Risk From Glance Patterns in Naturalistic Driving

2012 ◽  
Vol 54 (6) ◽  
pp. 1104-1116 ◽  
Author(s):  
Yulan Liang ◽  
John D. Lee ◽  
Lora Yekhshatyan
Keyword(s):  
Motor Vehicle ◽  
Driver Distraction ◽  
Crash Risk ◽  
Design Of Algorithms ◽  
Data Set ◽  
The Road ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
The Relationship ◽  
Better Than

Objective: In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. Background: Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash risk lacks detailed quantification. Method: The authors compared 24 algorithms that varied according to how they incorporated three potential contributors to distraction—glance duration, glance history, and glance location—on how well the algorithms predicted crash risk. Results: Distraction estimated from driver eye-glance patterns was positively associated with crash risk. The algorithms incorporating ongoing off-road glance duration predicted crash risk better than did the algorithms incorporating glance history. Augmenting glance duration with other elements of glance behavior—1.5th power of duration and duration weighted by glance location—produced similar prediction performance as glance duration alone. Conclusions: The distraction level estimated by the algorithms that include current glance duration provides the most sensitive indicator of crash risk. Application: The results inform the design of algorithms to monitor driver state that support real-time distraction mitigation systems.

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