STRIDE Project C (Performance Measurement & Management using Connected & Automated Vehicle Data), Principal Investigator: Dr. Mohammed Hadi, FIU
In this day and age, we are beginning to see vehicles on our roads with several levels of connectivity and/or autonomy. And as these cars, buses or trucks start navigating alongside conventional vehicles, they will be an important source of data to provide the transportation professionals with better ways to estimate transportation system performance. To do this, methods will need to be developed for the new data to be gathered, assessed, and utilized.
A multi-university team led by Dr. Mohammed Hadi of Florida International University is doing just that. They are working on a STRIDE funded project to produce new performance measures derived from emerging technologies such as autonomous and connected vehicles (AV/CV). The project is titled “Performance Measurement and Management using Connected and Automated Vehicle Data”. The research team includes Dr. Virginia Sisiopiku from the University of Alabama at Birmingham and Dr. Siva Srinivasan of the University of Florida.
Transportation professionals and decision makers use performance measures when planning and developing a project as well as in the management and operations of the transportation system to estimate and predict how a transportation system performs. These performance measures are currently derived based on data collected using existing sources such as point detectors or sensors that are embedded in pavement or on the roadside that provide information such as speed, volume (number of vehicles), weather data, and more. However, there is a lack of data at the vehicle level that allow better assessment of mobility and safety than currently possible based on the aggregate data collected from existing sources. The availability of CV data, even at lower market penetrations, can be sufficient to support critical transportation performance measurement and management functions.
In this project, the research team developed a framework for using CV data to estimate measures to support agency processes. The project then assessed using CV data for different applications. For this purpose, the researchers utilized available real-world vehicle-level data as well simulated vehicle trajectories.
More specifically, the FIU team 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 for use by TMCs for short-term prediction of traffic conditions. These developments can be used to proactively activate operational plans to mitigate potential deterioration in mobility and safety performance.
As for the research team at UAB, they 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.
And finally, the UF team investigated methods 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. Currently, it is difficult to estimate the levels of emissions in performance measures, but with connected vehicles, detailed information on a vehicle by vehicle basis is available that will help in this determination.
As we prepare for the influx of emerging technologies and how they will impact the transportation system, the results from this project will help transportation professionals at state DOTs, metropolitan planning organizations, and cities to develop and activate strategies, tactics, and plans that will optimize their transportation system and processes.