Safe-D: Safety through Disruption

Projects: Big Data Analytics


Real-world Use of Automated Driving Systems and their Safety Consequences

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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.

Delving into Safety Considerations of E Scooters: A Case Study of Austin, Texas

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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.

Characterizing Level 2 Automation in a Naturalistic Driving Fleet

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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.

E-Scooter Safety Assessment and Campus Deployment Planning

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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.

Exploring Crowdsourced Monitoring Data for Safety

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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 as […]

Developing an Intelligent Transportation Management Center (ITMC) with a Safety Evaluation Focus for Smart Cities

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This project will develop an intelligent transportation management center (ITMC) that adopts automated video data analysis to evaluate safety.

Behavior-based Predictive Safety Analytics Phase II

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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.

Data Mining Twitter to Improve Automated Vehicle Safety

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This project seeks to understand the conversation about automated vehicles on Twitter through a network and natural language processing analysis.

Use of Disruptive Technologies to Support Safety Analysis and Meet New Federal Requirements

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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. ​​​​​​​

Development of an Infrastructure Based Data Acquisition System (iDAS) to Naturalistically Collect the Roadway Environment

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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. ​​​​​​​​​​​​

Automated Vehicle Behavior Monitoring for Vulnerability Management

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This project seeks to develop algorithms for identifying when a vehicle has been compromised in a cybersecurity attack, and new approaches to designing and evaluating such techniques.

Legal Tools for Barriers to Accessing Data Sets in the Age of AV/CV Technologies

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This project will the data ownership and privacy implications of big data collection and processing.

Big Data Visualization and Spatiotemporal Modeling of Aggressive Driving

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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.

Data Fusion for Non-Motorized Safety Analysis

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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.

Comparison of SHRP2 Naturalistic Driving Data to Geometric Design Speed Characteristics on Freeway Ramps

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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.

Motorcycle Crash Data Analysis to Support Implementation of a Concrete Barrier Containment Options for Errant Motorcycle Riders

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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.

Vehicle Occupants and Driver Behavior: An Assessment of Vulnerable User Groups

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This project seeks to better understand the impact of vehicle occupants in speeding driving behavior.

Behavior-based Predictive Safety Analytics – Pilot Study

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This project seeks to explore the possibilit​ies 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.

Identification of Railroad Requirements for the Future Automated and Connected Vehicle (AV/CV) Environment

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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.

Creating a Roadmap for Safe-D Research Themes and Application Areas: Future Directions in Disruptive Technology and Safety

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This project will examine disruptive technologies that could address critical transportation safety challenges in future years.

Sources and Mitigation of Bias in Big Data for Transportation Safety

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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.

Street Noise Relationship to Vulnerable Road User Safety

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This project will develop a method to evaluate street noise and documented crash rates on roadways.

K-12 STEM Program: Exploring the Science of Retroreflectivity

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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.

Data Mining to Improve Planning for Pedestrian and Bicyclist Safety

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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.

Big Data Methods for Simplifying Traffic Safety Analyses

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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.

Influences on Bicyclists and Motor Vehicles Operating Speed within a Corridor

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This project will investigate the influences on motor vehicle and bicyclist operations within a corridor.