Intersection Management Using In-Vehicle Speed Advisory/Adaptation: Final Report

Rakha, Hesham A.; Bichiou, Youssef; Hassan, Abdallah; Zohdy, Ismail H. · 2016 · ROSA P / Connected Vehicle/Infrastructure University Transportation Center

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Summary

This report presents research on optimizing intersection management for Connected Automated Vehicles (CAVs) using in-vehicle speed advisory and adaptation systems. Motivated by the potential for CAVs to enhance safety, reduce congestion, and lower emissions, the study aims to develop control algorithms that leverage vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. The primary objective is to ensure safe crossing of intersections while minimizing delay, fuel consumption, and stops, addressing the gap in mature algorithms required for mass field implementation. The research developed and validated four distinct algorithmic approaches through mathematical modeling, simulation, and analysis. First, the Intersection Cooperative Adaptive Cruise Control (iCACC) system was modeled using the Rakha-Pasumarthy-Adjerid car-following model and the Virginia Tech Comprehensive Power-based Fuel Consumption Model. This approach uses optimization to adjust vehicle speeds in designated zones approaching the intersection to avoid conflicts. Second, a comprehensive nonlinear model incorporating reservation schemes and road surface conditions was developed. Third, a fully distributed heuristic algorithm, the Isolated Intersection Zone Algorithm (IIZA), was created for real-time implementation without expensive infrastructure. Finally, two algorithms for multi-intersection networks were proposed: the Networked Intersection Zones Algorithm (NIZA), relying solely on V2V communication, and the Dual-Layered Algorithm (DLA), which combines local IIZA control with a distributed multi-agent system coordinating neighboring intersections. Simulations utilized INTEGRATION micro-simulation software and MATLAB, testing scenarios with traffic volumes ranging from 500 to 2,000 vehicles per hour and varying weather conditions. The results demonstrate significant performance improvements over traditional intersection controls, including traffic signals, all-way stop controls, and roundabouts. The iCACC system reduced average vehicle delay by up to 90% and fuel consumption by 45% compared to signalized intersections. Specifically, fuel consumption under iCACC was 33% lower than signal control, 45% lower than all-way stop control, and 11% lower than roundabout control. The heuristic IIZA algorithm also showed substantial delay reductions compared to First-In-First-Out (FIFO) logic. Analysis of inclement weather revealed that while rain and snow increased critical gaps and delays, higher levels of market penetration for CAVs mitigated these effects, with 100% penetration yielding the greatest benefits in both delay and fuel savings. The study concludes that connected vehicle technologies can significantly enhance intersection efficiency and environmental sustainability. By optimizing vehicle trajectories and speeds, the proposed algorithms eliminate the need for traditional stopping mechanisms, thereby reducing crash risks associated with human error and intersection conflicts. The findings support the adoption of CAVs and connected infrastructure as viable solutions for improving mobility and reducing emissions, providing a foundational framework for future real-time implementations in mixed traffic environments.

Key finding

The iCACC algorithm reduced average vehicle delay by up to 90% and fuel consumption by 45% compared to traditional traffic signal control.

Methodology

simulator

Provenance

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Topics

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• traffic density