STRIDE Project C

STRIDE Project C

Performance Measurement and Management Using Connected and Automated Vehicle Data

Dr. Mohammed Hadi, FIU

Research Team

Mohammed Hadi, Florida International University
Virginia Sisiopiku, University of Alabama at Birmingham
Noreen McDonald, University of North Carolina at Chapel Hill
Ruth Steiner, University of Florida

UTC Project Info

Project Description

Transportation system performance is a key component in congestion management as well as in setting agency priorities and making policy decisions. Fusing data from existing sources such as point sensors, automatic vehicle matching technologies, third party vendors, incident and work zone databases, weather data, video analytics, high resolution controller data, and management and control sources can provide important information for making such decisions. Emerging connected and automated vehicle technologies, shared autonomy, and shared mobility will significantly affect demand and supply. They will also increase data quantity and quality and produce new performance measures (door-to-door travel times, queue locations, vehicle trajectories) that cannot be obtained using existing data sources. The objective of this project is to develop novel performance measurement alternatives considering the availability of emerging vehicle technologies through Dedicated Short Range Communications (DSRC) and/or wide area cellular technology. These advances will improve policy decision making, optimize operations, and improve outcomes.


  • Method to estimate mobility, reliability, and environmental metrics using connected vehicle data – Developed a method for using connected vehicle data to estimate mobility, reliability, and environmental metrics that are currently being estimated using traditional (existing) sources. The estimated performance measures can be used by a system operator, planner, or an automated system to support decisions associated with these processes. The measurements can be also used to derive information for dissemination to travelers, third-party data aggregators, traveler information service providers, and other agencies.
  • Methods to estimate new mobility and safety metrics – Developed methods to estimate new mobility and safety metrics that cannot be estimated based on existing sources of data.  The methods can be used in real-time operations by traffic management centers (TMCs) to determine the traffic states on the freeway segments.  In addition, machine learning models were developed that can by used by TMCs for short-term prediction of traffic conditions that can be used to proactively activate operational plans to mitigate potential deterioration in performance.
  • Method to estimate pollutant emission – Developed a method to estimate pollutant emission based on limited amount of connected vehicle. These methods can be used in off-line and real-time analysis of traffic conditions to determine the pollutant emission levels under different traffic conditions.  This can be used as part of the decisions to implement strategies and plans to reduce pollution.


Webinar – Performance Measurement and Management Using Connected and Automated Vehicle Data