| Project ID |
BITS-SRIP/F42CF6/2026 |
| Project Title |
Dynamic Eco-Driving Control for Partially Connected Automated Vehicles in Signalized Urban Corridors |
| Project Description |
This project proposes a dynamic temporal–spatial eco-approach and departure (EAD) control strategy for partially connected automated vehicles (CAVs) operating on signalized urban arterials under mixed traffic conditions. Unlike existing EAD approaches that assume full connectivity, isolated intersections, or fixed signal timings, the proposed methodology is designed for realistic deployment scenarios where CAVs coexist with human-driven vehicles (HDVs) and have access to limited signal phase and timing (SPaT) and vehicle-to-infrastructure (V2I) information.
At the arterial level, real-time SPaT data and link-level traffic states are used to construct feasible arrival time windows for CAVs at downstream intersections. For each equipped vehicle, a rolling-horizon optimization framework generates energy-efficient speed trajectories that jointly consider upstream departure, mid-link cruising, and downstream approach phases. The temporal component aligns vehicle arrival times with predicted green intervals, while the spatial component ensures smooth speed transitions across successive links, accounting for speed limits, safety constraints, car-following behavior, and interactions with HDVs.
Given partial connectivity, the methodology incorporates probabilistic state estimation to infer non-connected vehicle behavior and queue spillbacks. Eco-approach and departure trajectories are formulated as constrained optimal control problems, minimizing energy consumption and stop-related delay subject to acceleration, jerk, and safety constraints. The strategy dynamically updates speed advisories as vehicles progress along the arterial and as signal timing predictions evolve.
To assess system-level impacts, the proposed EAD strategy is evaluated across varying CAV penetration rates and traffic demand levels using microscopic simulation. Performance is benchmarked against baseline no-advisory scenarios and conventional single-intersection EAD methods, with metrics including travel time, number of stops, fuel consumption, emissions, and arterial throughput. The framework is explicitly designed to demonstrate how coordinated EAD under partial connectivity can yield corridor-level efficiency gains without requiring changes to existing signal control infrastructure.
Expected Outcomes:
(i) Energy-optimal eco-approach and departure trajectories under partial connectivity: The project will deliver a practical EAD control strategy capable of generating smooth, energy-efficient speed profiles for CAVs using limited SPaT and V2I information in mixed traffic environments.
(ii) Reduction in stops, delay and acceleration variability along arterials: Coordinated temporal-spatial speed control is expected to significantly reduce stop frequency and speed fluctuations, leading to improved travel time reliability and ride comfort.
(iii) Corridor-level benefits beyond equipped vehicles: By smoothing traffic flow and mitigating shockwaves, the proposed EAD strategy is expected to produce positive spillover effects for human-driven vehicles, improving overall arterial performance.
(iv) Robust performance across CAV penetration rates: The methodology will demonstrate measurable benefits even at low to moderate CAV market penetration, highlighting its feasibility during transitional deployment phases.
(v) Scalable and infrastructure-light deployment potential: The results will show that meaningful energy and mobility gains can be achieved without full connectivity or signal control modification, supporting near-term implementation in urban networks.
(vi) Contribution to sustainable urban mobility at the network level: Reduced fuel consumption, emissions and congestion along signalized corridors directly support sustainable transport objectives and SDG-aligned urban mobility goals. |
| Project Discipline |
Civil Engineering |
| Faculty Name |
Dr. Maripini Himabindu |
| Department |
Department of Civil Engineering |
