This research develops and evaluates optimal speed control strategies for Connected and Automated Vehicles (CAVs) at the network level, addressing critical gaps in existing research by incorporating multiple powertrain technologies including internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and hydrogen fuel cell vehicles (HFCVs). The study addresses real- world challenges such as communication delays, data transmission errors, and vehicle actuation complexities that are often overlooked in idealized research conditions. Using the INTEGRATION microscopic traffic simulation software, the research will implement advanced communication modules for vehicle-to-vehicle and vehicle-to-infrastructure interactions alongside vehicle speed control modules. The methodology involves formulating speed trajectory optimization as a constrained problem incorporating vehicle dynamics, fuel consumption models for different powertrains, and signal phase and timing data. Dynamic programming methods including A-star search algorithms will ensure real-time computational efficiency. The research includes extensive testing across varied traffic networks with different congestion levels and CAV market penetration rates, culminating in a scalable framework for generalizing results to large-scale networks including the entire U.S. roadway system through collaboration with Saudi Aramco.
Universities Involved
Virginia Tech
Principal Investigators
Hao Chen,
Hesham A. Rakha
Expected Research Outcomes & Impacts
The application of this research will transform urban traffic management by enabling transportation agencies to implement fuel-efficient, safety-enhanced CAV speed control strategies across diverse vehicle technologies and realistic operating conditions. Government agencies including the Department of Transportation will gain enhanced capabilities to develop forward-looking policies and regulations supporting CAV integration into urban traffic systems. Automotive manufacturers and technology developers will benefit from actionable insights for optimizing vehicle performance and reducing fuel consumption, driving innovation in CAV technologies and intelligent traffic management systems. The scalable framework will enable policymakers and transportation planners to assess CAV speed control impacts across the entire U.S. roadway system, supporting evidence-based decision-making for infrastructure investments. Urban communities will experience improved air quality through reduced emissions, enhanced traffic flow through optimized signal coordination, and increased safety through stop-and-go traffic mitigation. Long-term impacts include accelerated adoption of advanced CAV technologies, reduced fuel consumption across multiple powertrain types, and enhanced network-level traffic efficiency. The research outcomes will facilitate broader deployment of next-generation mobility solutions, supporting the transition to more efficient and cleaner transportation systems. Industry partnerships will ensure practical relevance and commercial viability, while the methodological frameworks will enable continued innovation in connected and automated vehicle technologies for improved urban mobility.
Subject Areas
Traffic Management, Intelligent Transportation Systems