Now showing items 1-20 of 63

    • 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 National University Transportation Center, 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 University Transportation Center, 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 Delivery Vehicle as a Disruptive Technology: How to Shape the Future with a Focus on Safety? 

      Das, Subasish; Tsapakis, Ioannis; Wei, Zihang; Elgart, Zachary; Kutela, Boniphace; Vierkant, Valerie; Li, Eric (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-09)
      The National Highway Traffic Safety Administration recently granted permission to deploy low-speed autonomous delivery vehicles (ADVs) on roadways. Although the mobility of ADVs is limited to low-speed roads and these ...
    • 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 A.; Machiani, Sahar Ghanipoor; Jahangiri, Arash; Dreger, Felix; de Winter, Joost (SAFE-D: Safety Through Disruption National University Transportation Center, 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 ...
    • Behavioral Indicators of Drowsy Driving: Active Search Mirror Checks 

      Meyer, Jason E.; Llaneras, Robert E. (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-07)
      Driver impairment due to drowsiness or fatigue has a significant impact on the safety of all road users. Assessing an impairment such as driver drowsiness through the use of vehicle-based technology continues to be an area ...
    • 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 University Transportation Center, 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 ...
    • Connected Vehicle Information for Improving Safety Related to Unknown or Inadequate Truck Parking 

      Katsikides, Nicole; Gick, Brittney N.; Parab, Smruti; Hwang, William "Billy"; Lee, Dahye; Montes de Oca, Jose Rivera; Farzaneh, Reza; Kong, Xiaoqiang "Jack"; Srisan, Tat; Bell, Stephen; Alden, Andy S.; Warner, Jeff; Schrank, David (Safe-D National UTC, 2022-10)
      Safety issues due to commercial truck parking shortages are a national concern. National hours-of-service (HOS) regulations limit drivers’ time on the road to increase safety by limiting fatigue. This requires drivers to ...
    • Crashworthiness Compatibility Investigation of Autonomous Vehicles with Current Passenger Vehicles 

      Dobrovolny, Chiara Silvestri; Stoeltje, Gretchen; Zalani, Aniruddha (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-11)
      Automated Vehicles have been one of the most sought-after concepts to make transportation more effective and safer. No-occupant vehicles with automated driving systems (ADS) make up one such class of vehicles. These are ...
    • Creating a Smart Connected Corridor to Support Research into Connected and Automated Vehicles 

      Brydia, Robert; Ruback, Leonard; Middleton, Dan (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-06)
      As connected and automated vehicle (CAV) technologies rapidly advance from concept to in-vehicle testing, real-world testbeds equipped with the appropriate technologies to support testing of these vehicles is a requirement.Testing ...
    • Curb Management Practices and Effectiveness in Improving Safety 

      Hansen, Todd; Elgart, Zachary; Bell, Stephen; Hu, Zhiheng; Wood, Nick; Alden, Andy (Safe-D National UTC, 2022-11)
      Curbside access has been a growing concern in cities over the last decade as on-demand passenger or goods transportation services have proliferated. Increased activity at key loading and unloading points may increase the ...
    • Data Fusion for Nonmotorized Safety Analysis 

      Sener, Ipek N.; Munira, Silvy; Zhang, Yunlong (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-08)
      This project explored an emerging research territory, the fusion of nonmotorized traffic data for estimating reliable and robust exposure measures. Fusion mechanisms were developed to combine five bike demand data sources ...
    • Data Mining to Improve Planning for Pedestrian and Bicyclist Safety 

      Jahangiri, Arash; Hasani, Mahdie; Sener, Ipek Nese; 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 E.; Roofigari-Esfahan, Nazila (SAFE-D: Safety Through Disruption National University Transportation Center, 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 ...
    • Detecting Pavement Distresses Using Crowdsourced Dashcam Camera Images 

      Dadashova, Bahar; Dobrovolny, Chiara Silvestri; Tabesh, Mahmood (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-05)
      Pavements play a vital role in the transportation infrastructure in the United States. Damage to public road transportation infrastructure causes roadways to fail to perform as intended and increases crash risks. Road ...
    • Development of a Connected Smart Vest for Improved Roadside Work Zone Safety 

      Roofigari-Esfahan, Nazila; White, Elizabeth E.; 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 University Transportation Center, 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 ...