Coordinated EV OD Demand Estimation and Traffic Sensor Placement in Smart Grid and Intelligent Transportation System

Estimating electric vehicle (EV) Origin-Destination (OD) demands based on collected traffic sensor data, along with strategically placing traffic sensors, are crucial for effective management in Smart Grid and Intelligent Transportation Systems. This paper proposes a pioneering approach that integrates equilibrium-based EV OD demand estimation and traffic sensor placement into a holistic framework. The proposed framework adopts tri-level structure to address the optimal placement of EV traffic sensors at the upper level, followed by a bilevel model for OD demand estimation through inverse optimization of the traffic assignment problem under equilibrium conditions. To overcome the inherent complexity of the tri-level framework, a deep learning-based solution approach is proposed. Deep reinforcement learning is employed to determine the optimal sensor placement, maximizing OD demand estimation accuracy within a predefined budget. Additionally, a novel encoder-decoder architecture with transformer networks approximates the solution to the bilevel inverse optimization problem. Leveraging multi-head self-attention mechanisms, transformer networks capture intricate relationships within collected EV traffic data. Case studies demonstrate the effectiveness of the proposed framework and the superiority of transformer networks. Results validate the accurate estimation of EV OD demands and highlight the transformative potential of deep learning in addressing traffic sensor placement and OD demand estimation challenges.