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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  • Evaluation of Various Surface Cleaning Techniques Inside Tunnels on Pavement Skid Resistance 

    Marcobal, José Ramon; Salado, Freddie; Flintsch, Gerardo W. (MDPI, 2021-09-28)
    A request started a comprehensive investigation on the Skid Resistance (SR) inside a highway tunnel in Madrid, Spain, to determine methods that will improve or maintain the SR above the minimum required level (60), after ...
  • Examining senior drivers' attitudes toward advanced driver assistance systems after naturalistic exposure 

    Liang, Dan; Lau, Nathan; Baker, Stephanie A.; Antin, Jonathan F. (Oxford University Press, 2020-01-01)
    Background and Objectives: The increasing number of senior drivers may introduce new road risks due to age-related declines in physical and cognitive abilities. Advanced driver assistance systems (ADAS) have been proposed ...
  • What Are the Restraint Practices, Preferences, and Experiences When Australian Parents Travel with Their Children in a Rideshare Vehicle? 

    Koppel, Sjaan; Peiris, Sujanie; Aburumman, Mohammed; Wong, Chernyse W. R.; Owens, Justin M.; Womack, Katie N. (MDPI, 2021-08-25)
    This study aimed to explore the preferences, experiences and restraint practices of Australian parents travelling with their children in rideshare vehicles. Six hundred and thirty-one participants completed an online survey ...
  • Decision-adjusted driver risk predictive models using kinematics information 

    Mao, Huiying; Guo, Feng; Deng, Xinwei; Doerzaph, Zachary R. (2021-06)
    Accurate prediction of driving risk is challenging due to the rarity of crashes and individual driver heterogeneity. One promising direction of tackling this challenge is to take advantage of telematics data, increasingly ...
  • Predicting Vehicle Trajectories at Intersections using Advanced Machine Learning Techniques 

    Jazayeri, Mohammad Sadegh; Jahangiri, Arash; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-05)
    The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety systems that aim to identify critical events such as near-crash situations and traffic violations. In a connected environment, ...
  • Data Mining Twitter to Improve Automated Vehicle Safety 

    McDonald, Anthony D.; Huang, Bert; Wei, Ran; Alambeigi, Hananeh; Arachie, Chidubem; Smith, Alexander Charles; Jefferson, Jacelyn (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-02)
    Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly ...
  • Development of a Connected Smart Vest for Improved Roadside Work Zone Safety 

    Roofigari-Esfahan, Nazila; White, Elizabeth; Mollenhauer, Michael A.; Talledo Vilela, Jean Paul (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-04)
    Roadside work zones (WZs) present imminent safety threats 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. A number of ...
  • Reference Machine Vision for ADAS Functions 

    Nayak, Abhishek; Rathinam, Sivakumar; Pike, Adam (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-05)
    Studies have shown that fatalities due to unintentional roadway departures can be significantly reduced if Lane Departure Warning and Lane Keep Assist systems are used effectively. However, these systems have not been ...
  • Design and Development of an Automated Truck Mounted Attenuator 

    White, Elizabeth E.; Mollenhauer, Michael A.; Talledo Vilela, Jean Paul (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-05)
    Truck-Mounted Attenuators (TMAs) are energy-absorbing devices added to heavy shadow vehicles to provide a mobile barrier that protects work crews from errant vehicles entering active work zones. While the TMA is designed ...
  • Alcohol Intoxication Checklist: A Naturalistic Approach 

    Wotring, Brian; Antin, Jon; Smith, Ryan C. (National Surface Transportation Safety Center for Excellence, 2021-07-16)
    This effort sought to determine the prevalence of particular visual and behavioral indicators for alcohol intoxication using data collected in the Strategic Highway Research Program 2 Naturalistic Driving Study (SHRP 2 ...
  • A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems 

    Elhenawy, Mohammed; Komol, Mostafizur R.; Masoud, Mahmoud; Liu, Shi Qiang; Ashqar, Huthaifa I.; Almannaa, Mohammed Hamad; Rakha, Hesham A.; Rakotonirainy, Andry (MDPI, 2021-07-06)
    Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such ...
  • Probability of detection of electric vehicles with and without added warning sounds 

    Roan, Michael J.; Neurauter, Luke; Song, Miao; Miller, Marty (2021-01-26)
    Detection performance as a function of distance was measured for 16 subjects who pressed a button upon aurally detecting the approach of an electric vehicle. The vehicle was equipped with loudspeakers that broadcast one ...
  • Analysis of Run-off-road Safety-critical Events in Virginia 

    Turturici, Marissa; Noble, Alexandria; Klauer, Charlie (National Surface Transportation Safety Center for Excellence, 2021-05-27)
    Run-off-road (ROR) crashes account for a large proportion of fatalities on U.S. roadways. ROR crashes usually involve a single vehicle and occur when the vehicle departs the roadway and then strikes an object. The research ...
  • Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models 

    Justo-Silva, Rita; Ferreira, Adelino; Flintsch, Gerardo W. (MDPI, 2021-05-07)
    Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is ...
  • Improving the Safety of Interactions Between Vulnerable Road Users and Automated Vehicles: A Collaborative Investigation 

    Owens, Justin M. (National Surface Transportation Safety Center for Excellence, 2021-04-28)
    This report documents a collaboration between researchers at the Pedestrian Bicycle Information Center at the University of North Carolina’s Highway Safety Research Center and the Virginia Tech Transportation Institute to ...
  • Examining Seniors’ Adaptation to Mixed Function Automated Vehicles: Analysis of Naturalistic Driving Data 

    Liang, Dan; Antin, Jonathan F.; Lau, Nathan K.; Stulce, Kelly E. (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-02)
    The study examined whether advanced driver assistance systems (ADAS) can benefit the mobility and driving performance of senior drivers. Two groups of driving data, collected separately from two naturalistic driving projects, ...
  • Use of Disruptive Technologies to Support Safety Analysis and Meet New Federal Requirements 

    Tsapakis, Ioannis; Das, Subasish; Khodadadi, Ali; Lord, Dominique; Morris, Jessica; Li, Eric (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-03)
    States are required to have access to annual average daily traffic (AADT) for all public paved roads, including non-federal aid system (NFAS) roadways. The expectation is to use AADT estimates in data-driven safety analysis. ...
  • Safety Impact Evaluation of a Narrow-Automated Vehicle-Exclusive Reversible Lane on an Existing Smart Freeway 

    Machiani, Sahar Ghanipoor; Jahangiri, Arash; Melendez, Benjamin; Katthe, Anagha; Hasani, Mahdie; Ahmadi, Alidad; Musial, Walter B. (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-02)
    This study fills the gap in the limited research on the effect of emerging Automated Vehicle (AV) technology on infrastructure standards. The main objective of this research is to evaluate implications of an innovative ...
  • Pediatric Vehicular Heatstroke: Evaluation of Preventative Technologies 

    Glenn, Laurel; Glenn, Eric; Neurauter, Luke (National Surface Transportation Safety Center for Excellence, 2021-04-06)
    In 2018 and 2019, pediatric vehicular heatstroke (PVH) was the leading cause of nontraffic child fatalities involving vehicles in the United States. Legislation is being introduced in Congress to require passenger vehicles ...
  • Preventing Crashes in Mixed Traffic with Automated and Human-Driven Vehicles 

    Talebpour, Alireza; Lord, Dominique; Manser, Michael; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
    Reducing crash counts on saturated road networks is one of the most significant benefits of autonomous vehicle (AV) technology. To date, many researchers have studied how AVs maneuver in different traffic situations, but ...

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