The Exploratory Advanced Research Program Fact Sheet

Utilizing Various Data Sources for Surface Transportation Human Factors Research

WORKSHOP SUMMARY REPORT   •   November 6-7, 2013

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EXECUTIVE SUMMARY

On November 6–7, 2013, at the Turner–Fairbank Highway Research Center in McLean, VA, the Federal Highway Administration’s (FHWA) Office of Safety Research and Development, with support from the Exploratory Advanced Research (EAR) Program, convened the workshop, ”Utilizing Various Data Sources for Surface Transportation Human Factors Research.” The workshop addressed the increasing number of different datasets and multiple ways of collecting data—from naturalistic driving and simulator studies to eye trackers and surveys—that can be used to increase an understanding of human errors.

Human errors are still a major cause of injuries and fatalities; however, a number of different datasets have recently become available to analyze human errors. These datasets point in different directions within different areas of interaction. Experts in human factors research, transportation safety, and driver behavior and performance analysis, met to discuss and determine which datasets were best and how one might resolve the differences. The information provided by the different datasets is sometimes complementary, sometimes competing, and sometimes confirmatory.  The workshop brought together a panel of experts to share their research experience of using multiple methods to gain insights about different aspects of driver and traveler behavior and performance. 

During day one of this workshop, participants heard seven presentations on using various datasets from sources such as driving simulators, field studies and field operational tests, and naturalistic driving studies. The experts discussed various methods to study behaviors that lead to errors and shared strategies they have deployed to gain insightful information about what datasets to use to target one or more human factors or behavior issues. The workshop also presented the idea of using multiple data collection methods to “cross-reference” analysis results, validate conclusions, and enhance the understanding of behaviors.

On day two of the workshop, an expert panel discussed issues related to consolidating data from multiple types of collection methods. The experts discussed how datasets must be carefully examined when combined from different sources. For example, some data sources are contradictory, leaving researchers with the need to conduct additional research to resolve the controversies. Alternatively, other data sources can be complementary and provide information in the field and in the laboratory on driver behaviors that point in a similar direction. How best to create complementary datasets also needs to be carefully considered. In addition, very few data sources are comprehensive, and they do not provide information on both driver behavior and crashes. The ability to develop models that can link behavioral datasets with crash datasets, leading to comprehensive datasets, is still in its infancy. The expert panel went on to identify several potential research topics to address the challenges that must be overcome to integrate data from multiple sources.

At the end of day two, the workshop sponsor divided the participants into three groups so that detailed discussion could be held to identify research gaps related to the following interactions of drivers: (1) with other road users, (2) with changing elements of the roadway and infrastructure, and (3) with their own vehicle. All three groups presented summaries of their discussion and recommendations to conclude this workshop.

Workshop panelists and participants noted two different ways of seeing how best to deal with multiple contradictory datasets, as follows:

• Bottom up—It is possible to take various known instances in which there are contradictions across datasets and identify why these inconsistencies arise and what can be done to avoid them in the future.
• Top down—A study across multiple sites would allow for the collection of various different types of data. It would then be possible to look for inconsistencies across sites in the same dataset and inconsistencies within sites across datasets.

Panelists were unanimous in recommending that there should be an attempt to understand how to use the different types of data in a study that includes the following components:

• Multiple sites (e.g. locations, geometries, traffic density, and environment).
• Multiple types of data gathered at each site (e.g., survey, simulator, and field).
• Multiple users (e.g., bicyclists, pedestrians, motorists, and drivers).
• Multiple methods of analysis (e.g., descriptive and inferential statistics, and quantitative behavioural models).

As part of the final workshop recommendations, participants identified many areas of priority for human factors research that could make use of the expanding datasets now available and soon to be available. These included modeling, safety, roadway departure, urban intersections, vehicle, pedestrian and bicyclist interaction, and data analysis. Participants suggested a number of specific items for further research, as follows:

• Evaluate the effectiveness of current signage used on roadways.
• Research speed perception.
• Develop solutions to improve roadway safety for pedestrians and bicyclists.
• Evaluate current Intelligent Transportation System technologies for pedestrian and bicycle safety.
• Develop a methodology to conduct research by using multiple data sources.
• Construct methods to measure exposure of pedestrians and bicyclists.

To further understanding and use of multiple data types, participants recommended a study, possibly focused at intersections, which includes multiple sites, multiple data types gathered at each site, multiple user types, and multiple methods of analysis. This study could provide critical information on how to resolve contradictions among datasets, how to put together complementary datasets that describe risky behaviors, and how to generate comprehensive datasets that link behaviors and crashes.

Table of Contents

Introduction

Day One: Presentations

• Driver–Driver and Other Road Users' Data for Human Factors Research
  Presented by Dr. Marco Dozza
• Driver and Pedestrian Recognition Behavior for Avoiding Conflict with Each Other at an Intersection
  Presented by Dr. Toru Hagiwara and Dr. Hidekatsu Hamaoka
• Driver–Infrastructure and Roadway Data for Human Factors Research
  Presented by Dr. Michael Manser
• Traffic Control Devices and In-Vehicle Systems
  Presented by Dr. Susan Chrysler
• Triangulating Data Sources to Understand Driver–Vehicle Behavior
  Presented by Dr. John Lee
• Driver–Vehicle Data for Human Factors Research
  Presented by Dr. Linda Ng Boyle

Day Two: Discussion and Summary

• Expert Panel Discussion
  Moderated by Dr. Donald Fisher
• Group Session Summary
• Workshop Recommendations

Appendices

• Appendix A: Agenda
• Appendix B: About the Presenters
• About the EAR Program

List of Figures

• Figure 1. An instrumented traditional bicycle.
• Figure 2. Average cycling behavior speeds.
• Figure 3. Cyclists' speed profile.
• Figure 4. An R-type accident.
• Figure 5. Front-passing diagram.
• Figure 6. Reproduced collision conflict between right-turning vehicle and pedestrian.
• Figure 7. Pedestrian–vehicle dedicated short-range communications system.
• Figure 8. Pedestrian crossing an experimental intersection.
• Figure 9. Real-world research environments.
• Figure 10. Expected research environments in terms of validity and experimental control.
• Figure 11. Adjusted research environments in terms of validity and experimental control.
• Figure 12. Tools and environments in relation to data sources.
• Figure 13. Experimental signs used in laboratory study.
• Figure 14. Trade-off between validity and experimental control.
• Figure 15. Research methods.
• Figure 16. Comparison of real maps and simulated scenarios.
• Figure 17. Impacts on driver response when using adaptive cruise control.
• Figure 18. Cluster analysis using simulation data.

List of Acronyms and Abbreviations

Introduction

Transportation safety is the top priority at the Federal Highway Administration (FHWA) and U.S. Department of Transportation (USDOT). A high percentage of transportation incidents and vehicle crashes are caused by human errors. As a result, it is important to continue investing in research resources to gain a comprehensive understanding of human errors and to try to answer the question, “Why do drivers and travelers do what they do?”

The motivation for this workshop was in large part a function of the increasing number of different datasets and multiple ways of collecting data—from naturalistic driving and simulator studies to eye trackers and surveys—that can be used to increase our understanding of human errors. Now is an ideal time to begin a discussion about how to resolve the differences and how to choose the best datasets for particular applications.

To initiate this discussion, FHWA's Office of Safety Research and Development, with support from the Exploratory Advanced Research (EAR) Program, convened the workshop, “Utilizing Various Data Sources for Surface Transportation Human Factors Research,” on November 6–7, 2013. Experts in transportation safety analysis and driver behavior and performance, were invited to the Turner–Fairbank Highway Research Center in McLean, VA, to share their research experience of using multiple methods to gain insights into different aspects of driver and traveler behavior and performance.

A primary question posed to researchers was how best to select the particular datasets most helpful for analyzing one of the following three major research topics: (1) the interaction between drivers and other road users, such as pedestrians and bicyclists; (2) the interaction between drivers and roadway and other transportation infrastructure; and (3) the interaction between drivers and their vehicles. This report captures highlights from the workshop and summarizes the discussions that took place.