Safe-D: Safety through Disruption

Signal Awareness Applications



Intersection collisions account for 40% of all crashes on U.S. roadways. It is estimated that 165,000 accidents, which result in approximately 800 fatalities annually, are due to vehicles that pass through intersections during red signal phases. Although infrastructure-based red-light violation countermeasures have been deployed, intersections remain a top location for vehicle crashes. The Virginia Department of Transportation and its research arm, the Virginia Transportation Research Council, partnered with the Virginia Tech Transportation Institute to create the Virginia Connected Corridors (VCC), a connected vehicle test bed located in Fairfax and Blacksburg, Virginia, that enables the development and assessment of early-stage connected and automated vehicle applications. Recently, new systems have been deployed that transmit position correction messages to support lane-level accuracy, enabling development of signal awareness applications such as red-light violation warning. This project enhances the current capabilities of VCC platforms by developing new signal awareness safety and mobility features. Additionally, this project investigated the technical and human factors constraints associated with user interfaces for notifying and alerting drivers to pertinent intersection-related information to curb unsafe driving behaviors at signalized intersections.

Project Highlights

  • Expanded on the current capabilities of the Virginia Connected Corridor (VCC) by development of a new signal awareness application.
  • Developed a Phase Service Remaining Android mobile application using SAE J2735 standard SPaT and MAP messages pulled from VCC resources to digitally represent the connected signalized intersections the mobile user is approaching.
  • Developed mobile application provides the basis in which a future in-depth controlled evaluation with naïve participants could be conducted.
  • Project provided graduate student the basis for their Ph.D. and enabled the gathering of data, analysis upon which further research and publications were built.
  • Project contributed to development of peer reviewed journal and research paper for a transportation conference.

Final Report

Report VTTI-00-021

EWD & T2 Products

To demonstrate the proof-of-concept application, the research team created a video showcasing the graphical user interface traversing roadways in the VCC.  This video can be found hosted at:

An Android application was developed and is downloadable only by developer invite on Google Play Store.  The app will need to go through a Google Play Store review process before it can be downloaded by the public.  It is the intent of the team to put the app through the review process after an eventual next phase project which would enhance the app for broader assessment in a naturalistic study.

Student Impact Statement(pdf): A PhD student received funding under this project (Seifeldeen Eteifa, a Civil and Environmental Engineering student with a specialization in Transportation Infrastructure and Systems Engineering from Virginia Tech. This file contains a statement by Eteifa as to the impact this project had on their education and workforce development.


Eteifa, S.*, H. A. Rakha, and H. Eldardiry. Predicting Coordinated Actuated Traffic Signal Change Times using Long Short-Term Memory Neural Networks. In Transportation Research Record: Journal of the Transportation Research Board, No. 2675, TRB, National Research Council, Washington, D.C., 2021, pp. 127–138.

Eteifa S.*, Rakha H.A., and Eldardiry H. (2021), “Predicting Coordinated Actuated Traffic Signal Change Times using LSTM Neural Networks,” Transportation Research Board (TRB) 100th Annual Meeting, Washington DC, Jan. 25-29. [Paper: TRBAM-21-02753]

Final Dataset

The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/Q9DLFK

Research Investigators (PI*)

Mike Mollenhauer (VTTI)*
Reginald Viray (VTTI)
Zac Doerzaph (VTTI)
Elizabeth White (VTTI)
Miao Song (VTTI)
Hesham Rekha (VTTI)
Seifeldeen Eteifa (VTTI)

Project Information

Start Date: 2019-02-01
End Date: 2022-08-31
Status: Complete
Grant Number: 69A3551747115
Total Funding: $303,214
Source Organization: Safe-D National UTC
Project Number: VTTI-00-021

Safe-D Theme Areas

Connected Vehicles
Automated Vehicles

Safe-D Application Areas

Vehicle Technology
Risk Assessment
Vulnerable Users
Driver Factors and Interfaces
Infrastructure Technology
Performance Measures

More Information

UTC Project Information Form

Sponsor Organization

Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States

Performing Organization

Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061