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Researcher profile

Yongjie Fu

Yongjie Fu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningArtificial Intelligenceeess.SYMultiagent SystemsSystems and Control

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Published work

3 published item(s)

preprint2026arXiv

Multi-Pedestrian Safety Warning at Urban Intersections Use Case of Digital Twin

Digital twins (DTs) for urban transportation systems have gained increasing attention; however, their systematic evaluation in safety-critical scenarios remains limited. This paper presents a multi-pedestrian safety warning system at urban intersections enabled by a tightly coupled physical-digital twin framework. Built upon the COSMOS city-scale wireless testbed in New York City, the proposed system integrates camera and ultra-wideband (UWB), edge-cloud computing, predictive trajectory modeling, and MQTT-based communication to deliver real-time safety alerts to vulnerable road users (VRUs). The system is evaluated through both field deployment and virtual reality (VR) experiments. Results demonstrate high warning generation accuracy, localization accuracy, efficient end-to-end latency under different model configurations, and significant reductions in user response time when warnings are issued. The proposed DT framework provides a scalable, modular, and generalizable solution for real-time multi-pedestrian safety enhancement at complex urban intersections.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles

This paper develops a decentralized reinforcement learning (RL) scheme for multi-intersection adaptive traffic signal control (TSC), called "CVLight", that leverages data collected from connected vehicles (CVs). The state and reward design facilitates coordination among agents and considers travel delays collected by CVs. A novel algorithm, Asymmetric Advantage Actor-critic (Asym-A2C), is proposed where both CV and non-CV information is used to train the critic network, while only CV information is used to execute optimal signal timing. Comprehensive experiments show the superiority of CVLight over state-of-the-art algorithms under a 2-by-2 synthetic road network with various traffic demand patterns and penetration rates. The learned policy is then visualized to further demonstrate the advantage of Asym-A2C. A pre-train technique is applied to improve the scalability of CVLight, which significantly shortens the training time and shows the advantage in performance under a 5-by-5 road network. A case study is performed on a 2-by-2 road network located in State College, Pennsylvania, USA, to further demonstrate the effectiveness of the proposed algorithm under real-world scenarios. Compared to other baseline models, the trained CVLight agent can efficiently control multiple intersections solely based on CV data and achieve the best performance, especially under low CV penetration rates.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment

This paper aims to develop a paradigm that models the learning behavior of intelligent agents (including but not limited to autonomous vehicles, connected and automated vehicles, or human-driven vehicles with intelligent navigation systems where human drivers follow the navigation instructions completely) with a utility-optimizing goal and the system's equilibrating processes in a routing game among atomic selfish agents. Such a paradigm can assist policymakers in devising optimal operational and planning countermeasures under both normal and abnormal circumstances. To this end, we develop a Markov routing game (MRG) in which each agent learns and updates her own en-route path choice policy while interacting with others in transportation networks. To efficiently solve MRG, we formulate it as multi-agent reinforcement learning (MARL) and devise a mean field multi-agent deep Q learning (MF-MA-DQL) approach that captures the competition among agents. The linkage between the classical DUE paradigm and our proposed Markov routing game (MRG) is discussed. We show that the routing behavior of intelligent agents is shown to converge to the classical notion of predictive dynamic user equilibrium (DUE) when traffic environments are simulated using dynamic loading models (DNL). In other words, the MRG depicts DUEs assuming perfect information and deterministic environments propagated by DNL models. Four examples are solved to illustrate the algorithm efficiency and consistency between DUE and the MRG equilibrium, on a simple network without and with spillback, the Ortuzar Willumsen (OW) Network, and a real-world network near Columbia University's campus in Manhattan of New York City.

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