C3PO: Connected Cars & Corridors for Pervasive Sensing and COntrol of Vehicular Flows


This research studies the behavior and performance of a transportation corridor made of a freeway and arterial streets under a Connected Vehicles (CV) environment, where vehicles equipped with wireless communication and sensing devices collect, process, and share traffic information among themselves along with roadside sensors. First, methods of fusing/combining real-time traffic data from both vehicles and roadside sensors will be explored to automate the detection of incidents (e.g., accidents), to estimate the number of cars waiting in line and to populate the tables that contain the number of trips from origins to destinations. Second, the research studies how congestion, particularly traffic jams, emerge and spread, and how this information can help drivers cope with congestion. Finally, the research makes use of the results obtained from the first two tasks to explore algorithms that will enable the adaptive, coordinated control of freeway ramp meters and nearby arterial traffic lights, and the re-routing of traffic in response to traffic incidents.

As part of this project, we will also explore the use of Cooperative Adaptive Cruise Control (CACC or ACC) technology to dramatically increase the productivity of freeway systems, where CACC technology can produce the greatest impact. Our envisioned system will integrate WAVE/DSRC, vehicle positioning, obstacle detection, and in-vehicle processors to form opportunistically high-performance vehicle streams on-demand, particularly at merging, lane-dropping locations and on special lanes in a distributed manner.

We hope the results of our research will lead to new ways to monitor and control vehicular traffic, which will enable applications for reducing traffic congestion and fuel consumption. We will build on some of the results and tools developed in our previous collaborative project, VMesh/VGrid, where our team leverages vehicular ad hoc networks (VANET) to perform distributed data sensing, relaying, and computing.



  • M. Zhang, Civil & Environmental Engineering (PI)
  • D. Ghosal, Computer Science (Co-PI)
  • C-N. Chuah, Electrical & Computer Engineering (Co-PI)

Graduate Students

  • Mani Amoozadeh, Electrical & Computer Engineering
  • Hui Deng, Civil & Environmental Engineering
  • Huajun Chai, Civil & Environmental Engineering


  • Kartik Pandit, Computer Science (PhD, 2013)



This work is supported by the National Science Foundation Grant CMMI-1301496