Integrated Back of Queue Estimation and Vehicle Trajectory Optimization Considering Uncertainty in Traffic Signal Timings

This research develops and evaluates an Eco-Cooperative Adaptive Cruise Control system in proximity of traffic signals (ECO-CACC-I) that estimates the spatiotemporal back of queue propagation in real-time, using loop detector and probe vehicle data, while considering the inherent uncertainties in actuated traffic signal timings. The system uses probe vehicle information as well as information from the control logic to enhance the real-time queue length estimates and does not require historical queue data. Through comprehensive simulation experiments involving a single vehicle approaching a traffic signal, as well as simulations of a signalized intersection covering various market penetration levels (MPLs) and varying demand levels, the fuel savings achieved by the ECO-CACC-I system are quantified. The developed queue estimation algorithm provides a significant improvement over the traditional use of shockwave theory alone. In addition, results of the ECO-CACC-I system demonstrate average fuel savings of up to 10.57% and 18.89%, respectively without and with consideration of the queueing process from the perspective of an individual vehicle. Furthermore, an average fuel saving of 16.5% is achieved for the isolated intersection simulations at a 100% MPL. While significant marginal savings are achieved from a single-vehicle perspective when considering the queue, this is not the case for a network-wide perspective, where the consideration of the queue process only results in a 2% overall enhancement in terms of fuel savings. This research quantifies the benefits and the limitations of ECO-CACC-I when considering surrounding traffic by incorporating real-time queue predictions for deployment in real-world traffic environments.

Recommended citation: A. K. Shafik and H. A. Rakha, "Integrated Back of Queue Estimation and Vehicle Trajectory Optimization Considering Uncertainty in Traffic Signal Timings," in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2024.3460375.
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