The Virginia Tech Transportation Institute (VTTI) conducts research to save lives, save time, save money and protect the environment. Researchers and students from multiple fields are continuously developing the techniques and technologies to solve transportation challenges from vehicular, driver, infrastructure and environmental perspectives.

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  • AR DriveSim: An Immersive Driving Simulator for Augmented Reality Head-Up Display Research 

    Gabbard, Joseph L.; Smith, Missie; Tanous, Kyle; Kim, Hyungil; Jonas, Bryan (2019-10-23)
    Optical see-through automotive head-up displays (HUDs) are a form of augmented reality (AR) that is quickly gaining penetration into the consumer market. Despite increasing adoption, demand, and competition among manufacturers ...
  • Optimizing the Lateral Wandering of Automated Vehicles to Improve Roadway Safety and Pavement Life 

    Zhou, Fujie; Hu, Sheng; Xue, Wenjing; Flintsch, Gerardo (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-12)
    Because most automated vehicles (AVs) are programmed to follow a set path and maintain a lateral position in thecenter of the lane, over time significant pavement rutting will occur. This study directly measured AV ...
  • Vehicle Occupants and Driver Behavior: A Novel Data Approach to Assessing Speeding 

    Martin, Michael W.; Green, Lisa L.; Shipp, Eva; Chigoy, Byron; Mars, Rahul (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-11)
    The question of whether driver behavior, and speeding in particular, differs based on passenger(s) presence requires the use of large amounts of data, some of which may be difficult to accurately obtain. Traditional methods ...
  • Implications of Truck Platoons for Roadside Hardware and Vehicle Safety 

    Dobrovolny, Chiara S.; Untaroiu, Costin; Sharma, Roshan; Jin, Hanxiang; Meng, Yunzhu (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-10)
    Platooning is an extension of cooperative adaptive cruise control and forward collision avoidance technology, which provides automated lateral and longitudinal vehicle control to maintain short following distances and tight ...
  • Data Mining to Improve Planning for Pedestrian and Bicyclist Safety 

    Jahangiri, Arash; Hasani, Mahdie; Sener, Ipek N.; Munira, Sirajum; Owens, Justin; Appleyard, Bruce; Ryan, Sherry; Turner, Shawn M.; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-11)
    Between 2009 and 2016, the number of pedestrian and bicyclist fatalities saw a marked trend upward. Taken together, the overall percentage of pedestrian and bicycle crashes now accounts for 18% of total roadway fatalities, ...
  • Use of Life Cycle Cost Analysis and Multiple Criteria Decision Aid Tools for Designing Road Vertical Profiles 

    Loulizi, Amara; Bichiou, Youssef; Rakha, Hesham (MDPI, 2019-12-12)
    The current design practice for the vertical profile of roads in rolling and mountainous terrains is to follow the existing grades in order to minimize earthwork costs. This means that the only criterion considered during ...
  • VTTI Annual Report, 2019 Fiscal Year 

    Unknown author (Virginia Tech Transportation Institute (VTTI), 2019)
    Learn more about VTTI's accomplishments during fiscal year 2019.
  • Weather Camera 

    Palmer, Matthew; Gibbons, Ronald (National Surface Transportation Safety Center for Excellence, 2019-11-18)
    Fog- and weather-related visibility reduction is a common cause of multiple-vehicle crashes. Large differential speeds and a tendency of vehicle operators to drive faster than is safe can lead to terrible crashes. Fog can ...
  • 4U Lighting – Cooperative Headlighting 

    Palmer, Matthew; Tsuda, Hiroshi; Williams, Brian; Gibbons, Ronald (National Surface Transportation Safety Center for Excellence, 2019-10-31)
    The purpose of this project was to evaluate the effectiveness of an alternative cooperative headlighting method, dubbed 4U Lighting. A human-subjects study was conducted in which 12 participants 65 or older observed ...
  • Motorcycle Crash Data Analysis to Support Development of a Retrofit Concrete Barrier System for Freeway Ramps 

    Wilson, Jonathan; Sulaica, Heather; Dobrovolny, Chiara Silvestri; Perez, Marcie (Safe-D: Safety Through Disruption, 2019-07)
    This project was intended to review the most relevant national and international studies, as well as protocols and standards that were developed to support motorcycle safety on roadways. In addition, crash data analysis ...
  • Design and Evaluation of a Connected Work Zone Hazard Detection and Communication System for Connected and Automated Vehicles (CAVs) 

    Mollenhauer, Michael; White, Elizabeth; Roofigari-Esfahan, Nazila (Safe-D: Safety Through Disruption, 2019-08)
    Roadside work zones (WZs) present imminent safety hazards for roadway workers as well as passing motorists. In 2016, 764 fatalities occurred in WZs in the United States due to motor vehicle traffic crashes, which are the ...
  • Older Drivers and Transportation Network Companies: Investigating Opportunities for Increased Safety and Improved Mobility 

    Tooley, Melissa; Zmud, Johanna; Ettelman, Benjamin; Moran, Maarit; Higgins, Laura; Shortz, Ashley; Wheeler, Eric (Safe-D: Safety Through Disruption, 2019-06)
    Transportation network companies (TNCs) such as Uber and Lyft offer an increasingly popular alternative to driving a personal vehicle. This project investigated the potential of TNCs to increase the safety and enhance the ...
  • Driver Training Research and Guidelines for Automated Vehicle Technology 

    Manser, Michael P.; Noble, Alexandria M.; Machiani, Sahar Ghanipoor; Shortz, Ashley; Klauer, Sheila G.; Higgins, Laura; Ahmadi, Alidad (Safe-D: Safety Through Disruption, 2019-07)
    The advent of advanced driver-assistance systems presents the opportunity to significantly improve transportation safety. Complex sensor-based systems within vehicles can take responsibility for tasks typically performed ...
  • Emerging Legal Issues for Transportation Researchers Using Passively Collected Data Sets 

    Stoeltje, Gretchen; Moran, Maarit; Zmud, Johanna; Ramsey, Nijm; Stibbe, Jayson (Safe-D: Safety Through Disruption, 2019-08)
    With the advent of new technologies to gather and process data, large data sets are being collected that are of interest to transportation researchers. However, legal and ethical questions around data ownership and protection ...
  • Examining Senior Drivers Adaptation to Mixed Level Automated Vehicles: A Naturalistic Study 

    Liang, Dan; Antin, Jonathan F.; Lau, Nathan K.; Stulce, Kelly E.; Baker, Stephanie A.; Wotring, Brian (Safe-D: Safety Through Disruption, 2019-08)
    Advances in the development of advanced vehicle technologies (AVTs), such as blind spot alerts, lane keep assist,lane alert, and adaptive cruise control, can benefit senior drivers by reducing exposure to hazards andcompensating ...
  • Behavior-based Predictive Safety Analytics – Pilot Study 

    Engström, Johan; Miller, Andrew; Huang, Wenyan; Soccolich, Susan; Machiani, Sahar Ghanipoor; Jahangiri, Arash; Dreger, Felix; de Winter, Joost (Safe-D: Safety Through Disruption, 2019-04)
    This report gives an overview of the main findings from the Behavior-based Predictive Safety Analytics – Pilot Study project. The main objective of the project was to investigate the possibilities of developing statistical ...
  • Factors Surrounding Child Seat Usage in Rideshare Services 

    Owens, Justin M.; Womack, Katie N.; Barowski, Laura (Safe-D: Safety Through Disruption, 2019-09)
    This project represents a collaborative, multimodal effort to understand the current state of child passenger safety with respect to rideshare vehicles, with the aim of using this information to develop an effective set ...
  • Analyzing Highway Safety Datasets: Simplifying Statistical Analyses from Sparse to Big Data 

    Lord, Dominique; Geedipally, Srinivas Reddy; Guo, Feng; Jahangiri, Arash; Shirazi, Mohammadali; Mao, Huiying; Deng, Xinwei (Safe-D: Safety Through Disruption, 2019-07)
    Data used for safety analyses have characteristics that are not found in other disciplines. In this research, we examine three characteristics that can negatively influence the outcome of these safety analyses: (1) crash ...
  • Developing a Neural–Kalman Filtering Approach for Estimating Traffic Stream Density Using Probe Vehicle Data 

    Aljamal, Mohammad A.; Abdelghaffar, Hossam M.; Rakha, Hesham A. (MDPI, 2019-10-07)
    This paper presents a novel model for estimating the number of vehicles along signalized approaches. The proposed estimation algorithm utilizes the adaptive Kalman filter (AKF) to produce reliable traffic vehicle count ...
  • Pediatric Vehicular Heatstroke: Review of Literature and Preventative Technologies 

    Glenn, E.; Glenn, L.; Neurauter, L. (National Surface Transportation Safety Center for Excellence, 2019-10-08)
    Pediatric vehicular heatstroke (PVH) was the leading cause of nontraffic child fatalities in the United States in 2018. On average, there are 38 PVH fatalities in the U.S. each year, for a total of 905 child fatalities on ...

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