Theme Areas: Big Data Analytics, Connected Vehicles
Abstract Humans operating motor vehicles are often required to engage in decision-making while under substantial cognitive loads imposed by the driving environment itself. Experiencing elevated emotions can influence driver decision-making in a way that increases the risk to the safety of the driver and system performance. Emotion regulation techniques (ERTs) can be used to improve […]
Theme Areas: Big Data Analytics, Connected Vehicles
Abstract Safety issues that stem from commercial truck parking shortages are a national concern. National hours-of-service (HOS) regulations limit drivers’ time on the road, in an attempt to increase safety by limiting fatigue; thereby, creating a need for drivers to locate safe, secure, and legal parking wherever they are when or before they hit […]
Theme Areas: Automated Vehicles, Big Data Analytics
Abstract Advanced driver assistance systems (ADAS) have significantly improved safety on today’s roadways but their impact may be limited by driver errors. Understanding and identifying these driver errors will require the integration of multi-domain datasets through predictive modeling and data integration approaches. The goals of this project are to identify relevant datasets for ADAS […]
Theme Areas: Big Data Analytics, Connected Vehicles
Abstract This project is inspired by major gaps identified in the literature pertaining to the work zone safety monitoring systems that leverage advanced technologies for tracking workers, identifying hazardous situations, and alerting individuals in danger. The existing systems have two key shortcomings. First, they either target safety hazards external to the work zone (e.g., only […]
Theme Areas: Automated Vehicles, Big Data Analytics
Abstract Distracted drivers are involved in approximately 4 million vehicle accidents each year in the U.S. (Dingus et al. 2016). These crashes result in many lives lost and billions of dollars in damages. This widespread issue has resulted in the adoption of regulations in the European Union that will require all new vehicles produced by mid-2022 to be equipped with driver monitoring […]
Theme Areas: Big Data Analytics, Transportation as a Service
Abstract COVID-19 has led to a reduction in vehicle miles traveled by motorized vehicles. Anecdotal evidence suggests that there may a shift to non-motorized modes. Getting more of the Virginia Tech community (including student, faculty, and staff) to walk, use the bus, carpool or ride bicycles for alternative transportation to decrease dependency on vehicle use […]
Theme Areas: Big Data Analytics, Transportation as a Service
Abstract The increased popularity of rideshare scooters was recently observed due to their availability, accessibility, and low cost. Benefits to their use include reduced traffic congestion and more environmentally friendly alternative to motor vehicles. However, there are some concerns regarding the safety of riders and the impacts these vehicles have on those who share roads […]
Theme Areas: Automated Vehicles, Big Data Analytics
Abstract 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 of interest. Both the initial detection, as well as continued monitoring, of driver drowsiness have been the emphasis […]
Theme Areas: Big Data Analytics, Connected Vehicles
Abstract The goal of the proposed project is to systematically extract traffic safety information from multiple complex sources of flood monitoring such as remote sensing technologies, flow gages, and weather stations, which can support informed planning for transportation safety against flooding in future smart cities. Flooding poses a significant hazard to the moving vehicles […]
Theme Areas: Big Data Analytics, Transportation as a Service
Abstract As rented and shared micromobility options, e-scooters are new and potentially transformative app-based modes that promise to alleviate first mile/last mile mobility issues, congestion, and more. Yet their safe deployment has not yet been systematically understood or standardized by users, cities, or operators. As of December 2019, 1,500 people had been injured and […]
Theme Areas: Big Data Analytics
Abstract This project supports a student in support of the National Cooperative Highway Research Program (NCHRP) Project 07-23 Access Management in the Vicinity of Interchanges and was led by Karen Dixon (TTI/TAMU)* and Maryam Shirinzadeh Dastgiri (TAMU). This project used a large volume of operational field data, micro-simulation data, and crash data to identify […]
Theme Areas: Automated Vehicles, Big Data Analytics, Connected Vehicles
This project will contribute to connected vehicles and ADS real-time safety monitoring, NDS data analysis, hail-driving driver safety prediction, as well as fleet and driver safety management programs.
Theme Areas: Big Data Analytics
Automated vehicle technologies (AV) have the potential to become one of the most highly disruptive technological applications of our century.
Theme Areas: Automated Vehicles, Big Data Analytics
This study will leverage data collected from 50 participants who drove personally owned vehicles equipped with ADSs for 12 months. The work is expected to contribute to a greater understanding of the prevalence and safety consequences of ADS use on public roadways, as well as drivers’ perception of the early production ADS.
Theme Areas: Big Data Analytics
Roadway construction and maintenance has become increasingly more common as the transportation system in the United States ages and the population and traffic volume increases.
Theme Areas: Big Data Analytics, Connected Vehicles
Today’s connected vehicles have an abundance of electronics and sensors that can passively collect data on driving behaviors, mechanical status, and physical roadway conditions.
Theme Areas: Automated Vehicles, Big Data Analytics, Transportation as a Service
This research project will investigate road user interactions with e-scooters.
Theme Areas: Big Data Analytics, Transportation as a Service
This case-study project will provide an in-depth examination of e-scooter safety considerations through a data-driven approach using Austin as the proposed study site.
Theme Areas: Automated Vehicles, Big Data Analytics
For this Safe-D project, dash video from the NOVA fleet collection effort will be analyzed using machine vision to, combined with additional approaches that offer some redundancy, determine the frequency, timing, and characteristics of L2 feature activations and deactivations.
Theme Areas: Big Data Analytics, Transportation as a Service
This project will deploy a fleet of e-scooters on the Virginia Tech campus through an exclusive, controlled research program which will collect data to assess safety impact, what behaviors are exhibited that may be beneficial or problematic, and ways in which kinematic and/or other data may be used to predict risky behavior and develop subsequent countermeasures.
Theme Areas: Big Data Analytics
Abstract This project encompasses four different activities to explore safety applications of emerging crowd-sourced data and datasets available from commercial aggregators. The first activity examines systems used to monitor and count pedestrian activity. Developing crash rates for these vulnerable users depends on knowing the volume of activity. Data from metropolitan planning organizations as well […]
Theme Areas: Big Data Analytics
This project will develop an intelligent transportation management center (ITMC) that adopts automated video data analysis to evaluate safety.
Theme Areas: Big Data Analytics
This project will analyze large scale continuous naturalistic data as well as event data, both public and proprietary, to study the role of different driving behaviors in the buildup of a safety critical event.
Theme Areas: Automated Vehicles, Big Data Analytics
This project seeks to understand the conversation about automated vehicles on Twitter through a network and natural language processing analysis.
Theme Areas: Big Data Analytics
This project seeks to examine whether traffic volume estimates developed from disruptive technologies such as cell phones, GPS/Bluetooth devices, and alternative data sources (e.g., demographic, socioeconomic, land use data) can be used confidently and accurately to support data-driven safety analysis (i.e., network screening) to meet the 2016 Highway Safety Improvement Program (HSIP) Final Rule requirements.
Theme Areas: Automated Vehicles, Big Data Analytics, Connected Vehicles
This project seeks to understand the existing systems and how they can be leveraged to provide the City with insight and suggested countermeasures to address the safety issues on these roadways.
Theme Areas: Automated Vehicles, Big Data Analytics, Connected Vehicles, Transportation as a Service
This project will the data ownership and privacy implications of big data collection and processing.
Theme Areas: Big Data Analytics
This project aims to develop a big data analytics framework and visualization tool to conduct spatiotemporal modeling and classify and visualize aggressive driving behavior using data from emerging technology.
Theme Areas: Big Data Analytics
This project will develop a framework which will bring together traditional and emerging data sources, and will be developed in such a way that it can be up- or down-scaled based on the available data sources of a study area. The exposure estimation output will then be used for crash assessment tailored to the needs of the study area.
Theme Areas: Big Data Analytics
This project will obtain and analyze detailed data – speed profiles along with selected driver and vehicle variables – from the SHRP2 NDS dataset for portions of trips that occurred on and near freeway ramps.
Theme Areas: Big Data Analytics
This project will review and analyze existing crash data on motorcycle related accidents, as well as to conduct a detailed literature review on existing motorcycle testing standards and various protocols that foreign Countries have developed throughout the years.
Theme Areas: Big Data Analytics
This project seeks to better understand the impact of vehicle occupants in speeding driving behavior.
Theme Areas: Big Data Analytics
This project seeks to explore the possibilities of using large sets of naturalistic crash and behavior data collected as part of commercial fleet- and behavior change management programs, collecting tens of thousands of crashes annually.
Theme Areas: Automated Vehicles, Big Data Analytics, Connected Vehicles
This project will examine freight and passenger railroad operational and infrastructure needs can be best considered in the development of future AV/CV system architecture.
Theme Areas: Automated Vehicles, Big Data Analytics, Connected Vehicles, Transportation as a Service
This project will examine disruptive technologies that could address critical transportation safety challenges in future years.
Theme Areas: Big Data Analytics
This project seeks to identify the sources of bias in big data for transportation safety planning and the approaches to mitigating bias in big data for passenger vehicles, transit, bicycling, and pedestrians.
Theme Areas: Big Data Analytics
This project will develop a method to evaluate street noise and documented crash rates on roadways.
Theme Areas: Big Data Analytics
This educational development project will take previously-devloped in-class activities that show real-world applications, link them to academic concepts and standards, and create curriculum and associated materials that can be used by teachers and other professionals across the nation.
Theme Areas: Big Data Analytics
This project will investigate data from multiple sources, including automated pedestrian and bicycle counters, video cameras, crash databases, and GPS/mobile applications (both active and passive monitoring), to inform bicycle and pedestrian safety improvements.
Theme Areas: Big Data Analytics
The project will evaluate statistical and other related methods that could simplify the analysis of the unique attributes related to safety and transportation-related big data, and present guidelines that can be used by researchers and practitioners for simplifying data analyses.
Theme Areas: Big Data Analytics
This project will investigate the influences on motor vehicle and bicyclist operations within a corridor.