Recent Articles
Dynamic Travel Time Prediction Using Pattern Recognition
Authors: Hao Chen, Hesham A. Rakha & Catherine C. McGhee
TranLIVE Collaborator: Virginia Polytechnic Institute and State University
Description: Travel-time information is an essential part of Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of travel times. From the perspective of travelers such information may assist in making better route choice and departure time decisions. For transportation agencies these data provide criteria with which to better manage and control traffic to reduce congestion. This study proposes a dynamic travel time prediction algorithm that matches current traffic patterns to historical data.
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A Short Range Vehicle to Infrastructure System at Work Zones and Intersections
Authors: Fengxiang Qiao, Jing Jia, and Lei Yu
TranLIVE Collaborator: Texas Southern University
Description: Traditional safety countermeasures at work zones include setting up special signs, installing barriers and a lower speed limit in work zones. For stop sign areas, usually our countermeasure is to remove all the obstructions. For signalized intersections, we usually improve the safety by setting up the signal lights in an optimized layout. However, many accidents still happen despite of these traditional methods. The purpose of this research is to identify how to improve the traffic safety and achieve better air quality in these areas by using RFID.
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Is Smart Growth Associated with Reductions in CO2 Emissions?
Authors: Xin Wang, Asad Khattak, Yichi Zhang
TranLIVE Collaborator: Old Dominion University
Description: The transportation sector is the second largest contributor to human-generated CO2 emissions. A key goal of the US Department of Transportation is to implement environmentally sustainable policies that can reduce carbon emissions from transportation sources. Smart growth developments are characterized by compact, mixed use, greater network connectivity and alternative mode friendly environments. These features may encourage reductions in vehicle travel and emissions. A better understanding of travel behavior in conventional and smart growth communities is needed to inform policies and make informed decisions.
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What is the Level of Volatility in Instantaneous Driving Decisions?
Authors: Asad Khattak, Xin Wang, Jun Liu, Golnush Masghati-Amoli and Sanghoon Son
TranLIVE Collaborator: Old Dominion University
Description: Instantaneous driving decisions are part of incessant human behavior during driving, strongly affecting safety outcomes, energy consumption and tailpipe emissions. To accommodate changes in surrounding environment, drivers make instantaneous decisions, such as maintaining speed, accelerating, braking, maintaining acceleration or deceleration, or increasing the rate of acceleration or deceleration (referred to as jerk, which is the decision to change the marginal rate of acceleration and deceleration). These instantaneous decisions and their combinations result in driving volatility. This paper develops a framework for understanding instantaneous decisions and explores volatility in such decisions with the aim of developing a fundamental understanding of instantaneous decisions.
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