Now showing items 1-20 of 39

    • Analysis of an Incentive-Based Smartphone Application for Young Drivers 

      Henk, Russell H.; Munira, Sirajum; Tisdale, Stacey (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-01)
      Traffic crashes remain the leading cause of unintentional youth deaths and injuries across the United States. Development of new and innovative interventions continues, with the aim of addressing this public health issue ...
    • 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 ...
    • Assessing Alternate Approaches for Conveying Automated Vehicle ‘Intentions’ 

      Basantis, Alexis; Miller, Marty; Doerzaph, Zachary R.; Neurauter, Luke (SAFE-D: Safety Through Disruption National UTC, 2020-05)
      One of the biggest highly automated vehicle (HAV) market barriers may be a lack of user trust in the automated driving system itself. Research has shown that this lack of faith in the system primarily stems from a lack of ...
    • Autonomous Emergency Navigation to a Safe Roadside Location 

      Furukawa, Tomonari; Zuo, Lei; Parker, Robert G.; Yang, Lisheng (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
      In this project, we developed essential modules for achieving the proposed autonomous emergency navigation function for an automated vehicle. We investigated and designed sensing solutions for safe roadside location ...
    • 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 ...
    • Big Data Visualization and Spatiotemporal Modeling of Risky Driving 

      Jahangiri, Arash; Marks, Charles; Machiani, Sahar Ghanipoor; Nara, Atsushi; Hasani, Mahdie; Cordova, Eduardo; Tsou, Ming-Hsiang; Starner, Joshua (SAFE-D: Safety Through Disruption National UTC, 2020-07)
      Statistical evidence shows the role of risky driving as a contributing factor in roadway collisions, highlighting the importance of identifying such driving behavior. With the advent of new technologies, vehicle kinematic ...
    • Data Mining to Improve Planning for Pedestrian and Bicyclist Safety 

      Jahangiri, Arash; Hasani, Mahdie; Sener, Ipek N.; Munira, Sirajum; Owens, Justin M.; 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, ...
    • 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 ...
    • 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 ...
    • Design and Evaluation of a Connected Work Zone Hazard Detection and Communication System for Connected and Automated Vehicles (CAVs) 

      Mollenhauer, Michael A.; 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 ...
    • 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 ...
    • Development of Analytic Method to Determine Weaving Patterns for Safety Analysis near Freeway Interchanges with Access Management Treatments 

      Dastgiri, Maryam Shirinzadeh; Dixon, Karen K. (SAFE-D: Safety Through Disruption National UTC, 2020-07)
      Urban arterials near freeway interchanges are vital elements of urban road infrastructures. They connect freeway network with high mobility and low access to urban network with lower mobility and higher access. This study ...
    • Driver Training Research and Guidelines for Automated Vehicle Technology 

      Manser, Michael P.; Noble, Alexandria M.; Machiani, Sahar Ghanipoor; Shortz, Ashley; Klauer, Charlie; 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 M.; 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 Ka Ching; Stulce, Kelly E.; Baker, Stephanie Ann; 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 ...
    • 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, ...
    • Exploring Crowdsourced Monitoring Data for Safety 

      Turner, Shawn M.; Martin, Michael; Griffin, Greg P.; Le, Minh; Das, Subasish; Wang, Ruihong; Dadashova, Bahar; Li, Xiao (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-03)
      This project included four distinct but related exploratory studies of data sources that could improve roadway safety analysis. The first effort evaluated passively gathered crowdsourced bicyclist activity data from ...
    • Exploring the Science of Retroreflectivity: Curriculum for Grades 4 through 6 

      Finley, Melisa D.; Hanover, Stephanie; Chrysler, Susan T. (Safe-D: Safety Through Disruption, 2018-10)
      According to the United States Department of Commerce, careers in science, technology, engineering, and mathematics (STEM) are growing faster than occupations in other areas. However, in-class academic concepts can seem ...
    • 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 ...
    • Formalizing Human Machine Communication in the Context of Autonomous Vehicles 

      Gopalswamy, Swaminathan; Saripalli, Srikanth; Shell, Dylan; Hickman, Jeff; Hsu, Ya-Chuan (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-05)
      There are many situations where tacit communication between drivers and pedestrians governs and enhances safety. The goal of this study was to formalize this communication and apply it to the driving strategy of an autonomous ...