CV-based adaptive signal control has potential to increase mainline arterial speeds by an average of 5 percent and reduce maximum intersection queue lengths by 66.7 percent even with low market penetration (5 percent CV).

University researchers develop algorithms and models to improve and evaluate operations on US-29 in Greenville, SC.

Summary Information

This study modeled a connected vehicle (CV) based adaptive traffic control system for a 3-mile-long section of US-29 in Greenville, SC. The system design assumed the corridor was equipped with multiple wireless communication devices (Roadside Units) having computation capabilities required to adjust signal timing parameters in real-time based on data collected from connected vehicles transmitting basic safety messages (time, location, speed, direction, etc.) in mixed traffic (CV and non-CVs) on the network.

A machine learning based short-term traffic forecasting model was used to predict the future traffic counts. Platoon movements were estimated and then a Mixed Integer Linear Programming (MILP) model was used to optimize signal intervals at each intersection.


Operational performance improved even for a low CV penetration (5 percent CV) and the benefit increased with increasing CV penetration.

5 percent CV market penetration
  • Average speed increased by 5.6 percent
  • Average delay decreased by 8 percent
  • Average maximum queue length decreased by 66.7 percent
  • Average stop delay decreased by 32.4 percent.

100 percent CV penetration
  • Average speed increased by 8.1 percent
  • Average delay decreased by 13.4 percent
  • Average maximum queue length decreased by 70.2 percent
  • Average stop delay decreased by 41.4 percent.

Benefit Comments

No comments posted to date

Comment on this Benefit

To comment on this summary, fill in the information below and click on submit. An asterisk (*) indicates a required field. Your name and email address, if provided, will not be posted, but are to contact you, if needed to clarify your comments.


Connected Vehicle Supported Adaptive Traffic Control for near-congested Condition in a Mixed Traffic Stream

Author: Khan, Sakib Mahmud and Mashrur Chowdhury

Published By: Cornell University



Average User Rating

1 (6 ratings)

Rate this Benefit

(click stars to rate)

Benefit ID: 2019-01385