201 citations · 922 across the 22 of their papers we have counts for
11 papers · 1 filter
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
Xuan Di, Rongye Shi, Zhaobin Mo +1
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)…
Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks
Zhaobin Mo, Yongjie Fu, Xuan Di
This paper aims to quantify uncertainty in traffic state estimation (TSE) using the generative adversarial network based physics-informed deep learning (PIDL). The uncertainty of t…
TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification
Zhaobin Mo, Yongjie Fu, Daran Xu +1
This paper proposes the TrafficFlowGAN, a physics-informed flow based generative adversarial network (GAN), for uncertainty quantification (UQ) of dynamical systems. TrafficFlowGAN…
A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation
Rongye Shi, Zhaobin Mo, Kuang Huang +2
Traffic state estimation (TSE) bifurcates into two categories, model-driven and data-driven (e.g., machine learning, ML), while each suffers from either deficient physics or small…
CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles
Mobin Zhao, Wangzhi Li, Yongjie Fu +2
This paper develops a decentralized reinforcement learning (RL) scheme for multi-intersection adaptive traffic signal control (TSC), called "CVLight", that leverages data collected…
A Physics-Informed Deep Learning Paradigm for Car-Following Models
Zhaobin Mo, Xuan Di, Rongye Shi
Car-following behavior has been extensively studied using physics-based models, such as the Intelligent Driver Model. These models successfully interpret traffic phenomena observed…
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment
Zhenyu Shou, Xu Chen, Yongjie Fu +1
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, o…
Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron
Zhenyu Shou, Ziran Wang, Kyungtae Han +3
Behavior prediction plays an essential role in both autonomous driving systems and Advanced Driver Assistance Systems (ADAS), since it enhances vehicle's awareness of the imminent…
When Do Drivers Concentrate? Attention-based Driver Behavior Modeling With Deep Reinforcement Learning
Xingbo Fu, Feng Gao, Jiang Wu
Driver distraction a significant risk to driving safety. Apart from spatial domain, research on temporal inattention is also necessary. This paper aims to figure out the pattern of…
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning
Zhenyu Shou, Xuan Di
A large portion of passenger requests is reportedly unserviced, partially due to vacant for-hire drivers' cruising behavior during the passenger seeking process. This paper aims to…
Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning
Zhenyu Shou, Xuan Di, Jieping Ye +3
Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, adding congestion and pollution into the road network and the environm…