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.
Made Public Date
08/22/2019

763

Greenville
South Carolina
United States
Identifier
2019-01385
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Connected Vehicle Supported Adaptive Traffic Control for near-congested Condition in a Mixed Traffic Stream

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.

FINDINGS

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.

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

Connected Vehicle Supported Adaptive Traffic Control for near-congested Condition in a Mixed Traffic Stream
Publication Sort Date
01/01/2019
Author
Khan, Sakib Mahmud and Mashrur Chowdhury
Publisher
Cornell University

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