VOLUME 3, ISSUE 3

ANNUAL MEETING

Features TTI Research

 

Texas A&M Transportation Institute (TTI) researchers provide a wealth of transportation safety research for their sponsors. This year at the Transportation Research Board’s (TRB’s) Annual Meeting Jan. 7–11 in Washington, D.C., TTI researchers presented their research to attendees from all transportation modes and both public and private agencies.

 

 

“The TRB Annual Meeting provides a great venue to showcase insights from our research and to interact with colleagues from around the world about safety,” said Robert Wunderlich, TTI senior research engineer and director of the Center for Transportation Safety. “Many TTI safety researchers help develop research needs through their work on TRB committees, and it gives all of us a chance to develop relationships with potential sponsors and collaborative partners.”

 

This year’s papers and presentations covered a wide variety of topics, such as understanding and predicting traffic fatalities, understanding teen driver behavior, and evaluating safety countermeasures. The papers are an example of the breadth and depth of safety research at TTI.

“The TRB Annual Meeting provides a great venue to showcase insights from our research and to interact with colleagues from around the world about safety,” said Robert Wunderlich, TTI senior research engineer and director of the Center for Transportation Safety.

 

 

Using Crash Prediction Models with Economic Factors for State Highway Safety Planning

Carol Flannagan and Daniel Blower of the University of Michigan Transportation Research Institute; Robert Wunderlich, Srinivas Geedipally, Dominique Lord, and Lingtao Wu of the Texas A&M Transportation Institute

 

This project describes two models of fatality counts: one model predicts annual fatality counts at the state level, and the other shows the change in fatality counts over the previous year at the state level. The two models then help set state-level projections for fatality counts (one model in Michigan and one in Texas). The Texas count model predicted a steady rise in fatalities, largely driven by an assumed increase in vehicle miles traveled (VMT) over the next five years. Michigan projections rose more gently but were driven by a more conservative estimate of VMT growth for that state. This approach could help states set more realistic targets for their strategic highway safety plans. However, the projections are only as good as the projected input, and researchers provide ways to improve the models for more widespread use.

 

In-Depth Investigation of Factors That Contributed to the Decline in Fatalities from 2008 to 2012 in the United States

Srinivas Geedipally, Robert Wunderlich, and Dominique Lord of the Texas A&M Transportation Institute; Daniel Blower and Carol Flannagan of the University of Michigan Transportation Research Institute

 

Between 2005 and 2011, the number of traffic fatalities in the United States declined by 11,031. Most of the dramatic decline occurred from 2008 to 2012, which also coincided with the great economic recession and aftermath. The objective was to study the relative influence of factors that contributed to this decline in fatalities. The most significant contributors were the substantial increase in teen and young adult unemployment, decrease in beer consumption, and reduction in gross domestic product and per capita income. Vehicle design improvements also contributed to the decline significantly, as did the decline in rural VMT and increased strictness of driving under the influence (DUI) laws. State highway spending was not a significant contributor to the drop. Changes in safety-belt-use rates and fuel prices were not significant contributors because they did not change much over the period.

 

 

An Incentive-Based Teen Driver Smartphone App: Results of 2017 Pilot Project

Sirajum Munira, Russell Henk, and Stacey Tisdale of the Texas A&M Transportation Institute

 

Traffic crashes continue to be the leading cause of unintentional death and injury of youth across the United States. New and innovative interventions continue to be developed to address this public health issue for this high-risk driving population. This project evaluated an incentive-based smartphone app developed by the Texas A&M Transportation Institute as part of the peer-to-peer safe driving program, Teens in the Driver Seat®. One of the core features of the app involves a reward system in which drivers earn points for miles driven without any phone interaction. The points can be redeemed for rewards and as a basis for competitions and achievement of safe driving levels. Researchers found significant reductions in distracted driving when incentives were awarded for distraction-free driving.

 

 

Knowledge about Crash Risk Factors and Self-Reported Driving Behavior: Exploratory Analysis on Multistate Teen Driver Survey

Lisa Minjares-Kyle, Subasish Das, Gabriella Medina, and Russell Henk of the Texas A&M Transportation Institute

 

Traffic crashes have been the leading cause of unintentional death for teen drivers for many years. Many challenges exist in determining the key risk factors, including conventional data sources:

 

•   Retrospective data sources are conventionally structured police reports, which are limited in information to identify risk factors at high levels of analysis.

•   Prospective data, such as from a survey, may add value in the current gap of identifying key risk factors associated with teen driver crashes.

 

The peer-to-peer safety program for young drivers, Teens in the Driver Seat® (TDS), uses positive peer influence and peer-to-peer education, which has significant impacts on high-risk behaviors. This study used surveys distributed through the TDS program and determined the perceived top risk factors from the survey respondents for each of the 11 states surveyed. The top perceived risk factors varied among male and female teen respondents. A cluster of male respondents more frequently responded with drinking, texting, phone use, speeding, and (lack of) seat belt use as top risks. Female respondents cited drinking, phone use, talking, music and eating.

 

 

 

 

Safety Evaluation of Alternative Audible Lane Departure Warning Treatments in Reducing Traffic Crashes: An Empirical Bayes Observational Before-After Study

Lingtao Wu, Srinivas R. Geedipally, and Adam M. Pike of the Texas A&M Transportation Institute

Roadway-departure crashes are a major contributor to traffic fatalities and injuries. Rumble strips are effective in reducing this type of crash. However, some roadway situations—for instance, inadequate shoulder width or roadway surface depth—have limited the use of conventional milled rumble strips. Alternative audible lane-departure warning systems include profile (audible) pavement markings and preformed rumble bars. This project examined the safety effectiveness of these alternative systems in reducing single-vehicle-run-off-road (SVROR) and opposite-direction (OD) crashes. The results revealed a 21.3 percent reduction in all SVROR and OD crashes, and 32.5 to 39.9 percent reduction in fatal and injury SVROR and OD crashes after installing profile pavement marking and preformed rumble bars.

 

 

Assessing Curve Severity and Crash Rates at Horizontal Curves on Rural Two-Lane Highways Using SHRP2 Safety Data

Lingtao Wu, Bahar Dadashova, Srinivas R. Geedipally, Michael P. Pratt, and Mohammadali Shirazi of the Texas A&M Transportation Institute

 

Horizontal curves are associated with a disproportionate number of severe crashes, particularly on two-lane rural highways. Factors that influence horizontal curve safety are speed compliance, geometric features of the curve, sight distance and traffic volume. The main objective of this study was to simultaneously assess curve severity using operational characteristics, and to assess crash rates at horizontal curves using safety data. Researchers calculated the severity of each curve using four methods, and the curve severity was compared to crash rates. The results suggest that for the higher curve severity categories, the greater the curve severity, the higher the crash rate. Safety analysts and roadway agencies should consider using Strategic Highway Research Program 2 data and curve severity assessment methods for addressing horizontal curve safety.

 

 

Modeling Animal-Vehicle Collisions Using Empirical Bayes Method Based on the Negative Binomial Models

Lingtao Wu of the Texas A&M Transportation Institute; Xiaoxue Yang, Yajie Zou, and Yinhai Wang

 

Transportation management agencies usually collect two common types of animal-vehicle collision (AVC) data: reported AVC data and carcass removal data. Previous studies found that these two data sets often demonstrate different characteristics. The objective of this study was to compare how the two data sets identify hotspots. Researchers analyzed data collected on 10 highways in Washington state. Results showed that the ranking of hotspots does significantly differ when using the reported AVC data and the carcass removal data.

 

 

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Srinivas Geedipally

 

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Lingtao Wu

 

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Russell Henk

 

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Robert Wunderlich