Developing and Field Implementing an Ecocruise Control System in the Vicinity of Traffic Signalized Intersections

Rakha, H.; Chen, H.; Almannaa, Mohammed; El-Shawarby, Ihab; Loulizi, Amara · 2016 · ROSA P / TranLIVE. University of Idaho

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Summary

This research addresses the high fuel consumption and greenhouse gas emissions associated with stop-and-go driving at signalized intersections. Motivated by the potential of connected vehicle technologies, the study develops and implements Eco-Cooperative Adaptive Cruise Control (Eco-CACC) algorithms designed to optimize vehicle trajectories for fuel efficiency. The work specifically targets gaps in existing literature by extending optimization from single to multiple intersections and addressing practical implementation challenges such as communication latency and driver behavior. The methodology combines microscopic traffic simulation and field testing. The authors utilized the INTEGRATION software to evaluate algorithm performance, incorporating the Rakha-Pasumarthy-Adjerid car-following model and kinematic wave theory to estimate queue lengths. They developed two primary algorithms: Eco-CACC-Q, which accounts for vehicle queues at isolated intersections, and Eco-CACC-MS, which optimizes speed profiles across multiple consecutive intersections. These algorithms compute advisory speed limits based on Signal Phasing and Timing (SPaT) data and queue dissipation times. Additionally, the system was implemented in an automated vehicle at the Virginia Smart Road Connected Vehicle Test Bed. Field tests involved 32 participants conducting 64 trips each under varying signal timings and road grades, comparing uninformed driving, manual audio-guided driving, and longitudinally automated control. Simulation results demonstrated that Eco-CACC-Q consistently outperformed algorithms ignoring queue effects. At a 100% market penetration rate, fuel consumption reductions reached 7% for the multi-intersection algorithm (Eco-CACC-MS-Q) and 4.2% for the single-intersection variant. Savings were sensitive to road configuration; on two-lane roads, fuel consumption increased when market penetration was below 30% due to lane-changing behaviors, but yielded 4.8% savings at higher penetration rates. In a four-intersection network, savings reached 7.7% on single-lane roads. Field tests confirmed significant real-world benefits: compared to uninformed driving, the longitudinally automated Eco-CACC system reduced fuel consumption by 37.8% and travel time by 9.3%. The study concludes that integrating queue estimation and multi-intersection coordination significantly enhances the fuel efficiency of connected vehicles. The findings highlight that algorithm performance is heavily influenced by market penetration rates, traffic demand, and signal timing parameters. The successful field implementation validates the technical feasibility of Eco-CACC systems, demonstrating that automated speed control can effectively smooth traffic flow and reduce energy consumption in the vicinity of signalized intersections.

Key finding

The longitudinally automated Eco-CACC system resulted in fuel consumption savings of up to 37.8 percent and travel time reductions of up to 9.3 percent compared to uninformed driving.

Methodology

mixed_methods

Sample size: 32

Provenance

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

Topics

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• eco driving