Kweon Y.-J.,Virginia Center for Transportation Innovation and Research
Journal of Safety Research | Year: 2015
Introduction Virginia saw a 20% reduction in traffic fatalities in 2008, an unprecedented annual reduction since 1950, and safety stakeholders in Virginia were intrigued about what caused such large a reduction and more generally what affects traffic safety from a macroscopic perspective. Method This study attempted to find factors associated with such a reduction using historical data of Virginia. Specifically, the study related 18 factors to seven traffic safety measures. Results In terms of annual changes, the study found that typical crash exposures were not generally associated with the seven measures, while two economic indicators (unemployment rate and U.S. Consumer Price Index [CPI]) were strongly associated with most of them. Conclusions Annual changes in the CPI and unemployment rate account for about half of the annual changes in total and fatal crash counts, respectively. On average, a 1 point increase in CPI and a 1% increase in the unemployment rate are associated with about 2,500 fewer traffic crashes and about 40 fewer fatal crashes annually in Virginia, respectively. © 2015 National Safety Council and Elsevier Ltd. All rights reserved.
Goodall N.J.,Virginia Center for Transportation Innovation and Research |
Smith B.L.,University of Virginia |
Park B.B.,University of Virginia
Journal of Intelligent Transportation Systems: Technology, Planning, and Operations | Year: 2016
Given the current connected vehicles program in the United States, as well as other similar initiatives in vehicular networking, it is highly likely that vehicles will soon wirelessly transmit status data, such as speed and position, to nearby vehicles and infrastructure. This will drastically impact the way traffic is managed, allowing for more responsive traffic signals, better traffic information, and more accurate travel time prediction. Research suggests that to begin experiencing these benefits, at least 20% of vehicles must communicate, with benefits increasing with higher penetration rates. Because of bandwidth limitations and a possible slow deployment of the technology, only a portion of vehicles on the roadway will participate initially. Fortunately, the behavior of these communicating vehicles may be used to estimate the locations of nearby noncommunicating vehicles, thereby artificially augmenting the penetration rate and producing greater benefits. We propose an algorithm to predict the locations of individual noncommunicating vehicles based on the behaviors of nearby communicating vehicles by comparing a communicating vehicle's acceleration with its expected acceleration as predicted by a car-following model. Based on analysis from field data, the algorithm is able to predict the locations of 30% of vehicles with 9-m accuracy in the same lane, with only 10% of vehicles communicating. Similar improvements were found at other initial penetration rates of less than 80%. Because the algorithm relies on vehicle interactions, estimates were accurate only during or downstream of congestion. The proposed algorithm was merged with an existing ramp metering algorithm and was able to significantly improve its performance at low connected vehicle penetration rates and maintain performance at high penetration rates. Copyright © Taylor & Francis Group, LLC.
Miller J.S.,Virginia Center for Transportation Innovation and Research
Journal of Urban Planning and Development | Year: 2012
Using Virginia's statewide multimodal plan as a case study, this paper outlines an approach to evaluating policies that coordinate the transportation-related efforts of individual agencies. The approach entails identification of seven potentially promising multimodal policies, a case study quantification of impacts for two such policies, and recommended steps for implementation. The case policies evaluated were (1) using cost per kilogram of emissions eliminated to select among eight alternatives and (2) increasing density to reduce CO 2 emissions. The case study demonstrates the feasibility of the outlined approach: Policy 1 increases efficacy by a factor of up to 3.7, and Policy 2 reduces annual CO 2 by 1.5 million metric tons, showing that a comparison of diverse multiagency policies at a sketch planning level is productive. The paper shows that a multimodal planner's role includes explicit identification of assumptions and quantitative methods that enable a comparison of diverse transportation investments given the typical lack of hard data early in the transportation planning process. © 2012 American Society of Civil Engineers.
Goodall N.,Virginia Center for Transportation Innovation and Research
Transportation Research Record | Year: 2014
Automated vehicles have received much attention recently, particularly the Defense Advanced Research Projects Agency Urban Challenge vehicles, Google's self-driving cars, and various others from auto manufacturers. These vehicles have the potential to reduce crashes and improve roadway efficiency significantly by automating the responsibilities of the driver. Still, automated vehicles are expected to crash occasionally, even when all sensors, vehicle control components, and algorithms function perfectly. If a human driver is unable to take control in time, a computer will be responsible for precrash behavior. Unlike other automated vehicles, such as aircraft, in which every collision is catastrophic, and unlike guided track systems, which can avoid collisions only in one dimension, automated roadway vehicles can predict various crash trajectory alternatives and select a path with the lowest damage or likelihood of collision. In some situations, the preferred path may be ambiguous. The study reported here investigated automated vehicle crashing and concluded the following: (a) automated vehicles would almost certainly crash, (b) an automated vehicle's decisions that preceded certain crashes had a moral component, and (c) there was no obvious way to encode complex human morals effectively in software. The paper presents a three-phase approach to develop ethical crashing algorithms; the approach consists of a rational approach, an artificial intelligence approach, and a natural language requirement. The phases are theoretical and should be implemented as the technology becomes available.
Winter K.,Virginia Center for Transportation Innovation and Research
Transportation Research Record | Year: 2014
In 2012 the Virginia Department of Transportation (DOT) Research Library conducted a trial of the electronic book database of EBSCO Information Services (EBSCO). The goals of the trial were to determine the level of interest by Virginia DOT employees in accessing e-books, to observe usage patterns and preferences of users in accessing content (EBSCO offered three access options for viewing onscreen or downloading to a personal device), to observe and gather usage statistics during the trial, to survey users on their experience and preferences for e-book devices, and to learn whether patrons used the library's subscriptions to the Books24x7, Knovel, or ASCE databases, which contain onscreen-only e-books. Usage statistics revealed high levels of interest in the e-book database. During the trial, 959 user sessions occurred, with 2,702 searches taking place, 694 e-books read onscreen, and 130 e-books checked out and downloaded to Adobe Digital Editions. Of the 32 respondents to a user satisfaction survey, 93.75% indicated that they would use e-books for their work or professional development; 39% found them somewhat easy or very easy to use; 63% read e-books onscreen; and 37% read from a portable e-book reader. Ninety percent of respondents had used other library full-text databases; 69% had purchased or received an e-book as a gift; and 40% had borrowed an e-book from another library. Research indicates that the EBSCO e-books database is a viable resource for Virginia DOT, provided that the proper content can be licensed and that adequate user education is provided.