Connected and Autonomous Vehicles (CAVs) allow the development of more advanced driving strategies with reduced safety risks and delays when passing intersections with mixed traffic flow. This project will develop advanced optimization strategies for traffic operations over transportation networks with CAVs under complicated dynamic traffic conditions. Human-factor experiments will be conducted via a stationary driving simulator to evaluate the impact of three independent scenarios on drivers’ driving performance under mixed traffic flow: (1) absence or presence of crossing pedestrians, (2) availability of real-time voice alert prompts from the Advanced Driver-Assistance System (ADAS), and (3) disabled or activated safety and cost-effective traffic signal control algorithms. The research team will recruit licensed adult drivers from the TSU campus community and local Houston residents, covering different age ranges, driving experience, and familiarity with CAVs, representing regular urban road users to ensure generalizable experimental outcomes. A full within-subject design is adopted, and all participants shall complete all test scenarios for direct performance comparison. The sample size should meet the standard statistical power (≥0.85) for transportation simulator studies. The optimized traffic control strategies for intersections with mixed traffic flow will help transportation agencies at all levels and car manufacturers to understand the design, operation, and impacts of optimal signal control strategies. The project will provide urgent science and test-based input to inform policy and practice development.
Optimization of Safe and Cost-Effective Driving Strategies with Connected and Autonomous Vehicles through Driving Simulator Tests
This project/product was funded by a grant awarded prior to 2025.