Using Connected Vehicle Technology for Advanced Signal Control Strategies

Kari, David; Wu, Guoyuan; Barth, Matthew · 2016 · ROSA P / National Center for Sustainable Transportation (NCST) (UTC)

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Summary

This research report addresses the limitations of conventional traffic signal control, which relies on point detection sensors that provide limited and often inaccurate data regarding traffic states. The authors propose an online adaptive signal control (ASC) strategy leveraging Connected Vehicle (CV) technology to optimize signal phase and timing based on real-time vehicle data. The study aims to reduce travel delay and fuel consumption by treating intersections as multi-agent systems where vehicle agents communicate with an intersection management agent. The methodology employs an agent-based framework using the PARAMICS microscopic traffic simulation software, integrated with EPA’s MOVES software for environmental metrics. The proposed CV-ASC strategy utilizes a flexible dual-ring controller modeled as a finite state machine, allowing for dynamic optimization of both phase duration and sequence. The system optimizes signal timing based on a queue length Measure of Effectiveness (MOE), defined by vehicles within communication range traveling below a specific speed threshold. To prevent "green starvation" for low-volume approaches, the algorithm incorporates quadratic aging factors that weight queue lengths based on the time elapsed since a phase was last served. The strategy was evaluated in two contexts: an isolated intersection and a corridor of intersections. Baseline comparisons included fixed-phase signal timing, Webster’s formula-based timing, and Highway Capacity Manual (HCM) methods. Sensitivity analyses were conducted across varying traffic volumes (1,000 to 6,000 vehicles per hour) and demand profiles. Results indicate that the CV-ASC strategy outperforms HCM-based methods and fixed-phase timing in both isolated and corridor settings, demonstrating robustness to traffic demand variations. For the isolated intersection, the CV strategy yielded the highest travel time and energy savings at low traffic volumes. As volume increased, benefits decreased, with savings eroding at near-saturated conditions (6,000 vehicles per hour). Emissions savings ranged from -5% to 15%, with positive savings at lower volumes due to reduced waiting times, and slight negative savings at high volumes due to increased stops as the intersection approached capacity. The study concludes that while conventional fixed timing performs adequately under saturated conditions, CV-based adaptive control offers significant advantages in reducing delay and fuel consumption under typical and low-volume traffic conditions.

Key finding

The connected vehicle adaptive signal control strategy reduced travel delay and fuel consumption compared to fixed phase and HCM-based baselines, with benefits decreasing as traffic volume approached intersection capacity.

Methodology

simulator

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• v2x connected vehicle