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For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In other words, how the physics is encoded into DNNs and how the physics and data components are represented. In this paper, we offer an overview of a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE), a central problem in transportation engineering. When observation data, problem type, and goal vary, we demonstrate potential architectures of PIDL computational graphs and compare these variants using the same real-world dataset. 

The development of more sustainable urban transportation is prompting the need for better energy management techniques. Connected electric vehicles can take advantage of environmental information regarding the status of traffic lights. In this context, eco-approach and departure methods have been proposed in the literature. Integrating these methods with regenerative braking allows for safe, power-efficient navigation through intersections and crossroad layouts. This paper proposes rule- and fuzzy inference system-based strategies for a coupled eco-approach and departure regenerative braking system. This analysis is carried out through a numerical simulator based on a three-degree-of-freedom connected electric vehicle model. The powertrain is represented by a realistic power loss map in motoring and regenerative quadrants. The simulations aim to compare both longitudinal navigation strategies by means of relevant metrics: power, efficiency, comfort, and usage duty cycle in motor and generator modes. Numerical results show that the vehicle is able to yield safe navigation while focusing on energy regeneration through different navigation conditions. 

Electric vehicles (EVs) have been used in the ride-hailing system in recent years, which brings the electric fleet management problem (EFMP) critical. This paper aims to leverage multi-agent reinforcement learning (MARL) in EFMP. In particular, we focus on how EVs learn to manage battery charging, pick up and drop off passengers. We propose an integrated SUMO-Gym framework based on the SUMO simulator to capture EVs’ asynchronous decisionmaking regarding charging and ride-hailing in complex traffic environments. We adopt a hierarchical reinforcement learning (HRL) scheme, where each EV decides to get charged or pick up a passenger on the upper level and chooses a charging station or passenger on the lower level. We develop a learning algorithm for the HRL scheme to solve EFMP and present numerical results about the efficiency of our algorithm and policies EVs have learned in EFMP. Our codes are available at https://github.com/LovelyBuggies/SUMO-Gym, which provides an open-source environment for researchers to design traffic scenarios and test RL algorithms for EFMP.