445 citations · 888 across the 21 of their papers we have counts for
22 papers
Uncertainty-aware Traffic Prediction under Missing Data
Hao Mei, Junxian Li, Zhiming Liang +3
Traffic prediction is a crucial topic because of its broad scope of applications in the transportation domain. Recently, various studies have achieved promising results. However, m…
Safety in Traffic Management Systems: A Comprehensive Survey
Wenlu Du, Ankan Dash, Jing Li +2
Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has int…
Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
Longchao Da, Minquan Gao, Hao Mei +1
Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and mitigate congestion waste. In recent, promising results ha…
Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control
Longchao Da, Hao Mei, Romir Sharma +1
Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people. Reinforcement Learning (RL) has shown promising results in optimizi…
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
Jiaxing Zhang, Zhuomin Chen, Hao Mei +3
Graph regression is a fundamental task that has gained significant attention in various graph learning tasks. However, the inference process is often not easily interpretable. Curr…
FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph
Zhanyu Liu, Chumeng Liang, Guanjie Zheng +1
This paper proposes the fine-grained traffic prediction task (e.g. interval between data points is 1 minute), which is essential to traffic-related downstream applications. Under t…
Reinforcement Learning Approaches for Traffic Signal Control under Missing Data
Hao Mei, Junxian Li, Bin Shi +1
The emergence of reinforcement learning (RL) methods in traffic signal control tasks has achieved better performance than conventional rule-based approaches. Most RL approaches req…
HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control
Dimitris M. Vlachogiannis, Hua Wei, Scott Moura +1
Single occupancy vehicles are the most attractive transportation alternative for many commuters, leading to increased traffic congestion and air pollution. Advancements in informat…
HMES: A Scalable Human Mobility and Epidemic Simulation System with Fast Intervention Modeling
Haoyu Geng, Guanjie Zheng, Zhengqing Han +2
Recently, the world has witnessed the most severe pandemic (COVID-19) in this century. Studies on epidemic prediction and simulation have received increasing attention. However, th…
SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control
Wenlu Du, Junyi Ye, Jingyi Gu +3
Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the…
LibSignal: An Open Library for Traffic Signal Control
Hao Mei, Xiaoliang Lei, Longchao Da +2
This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state…
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
Hua Wei, Jingxiao Chen, Xiyang Ji +11
This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on Honor of Kings, one of the world's most popular games at present. Compared to other e…
Modeling Network-level Traffic Flow Transitions on Sparse Data
Xiaoliang Lei, Hao Mei, Bin Shi +1
Modeling how network-level traffic flow changes in the urban environment is useful for decision-making in transportation, public safety and urban planning. The traffic flow system…
Boosting Offline Reinforcement Learning with Residual Generative Modeling
Hua Wei, Deheng Ye, Zhao Liu +5
Offline reinforcement learning (RL) tries to learn the near-optimal policy with recorded offline experience without online exploration. Current offline RL research includes: 1) gen…
Learning to Simulate on Sparse Trajectory Data
Hua Wei, Chacha Chen, Chang Liu +2
Simulation of the real-world traffic can be used to help validate the transportation policies. A good simulator means the simulated traffic is similar to real-world traffic, which…
How Do We Move: Modeling Human Movement with System Dynamics
Hua Wei, Dongkuan Xu, Junjie Liang +1
Modeling how human moves in the space is useful for policy-making in transportation, public safety, and public health. Human movements can be viewed as a dynamic process that human…
A Probabilistic Simulator of Spatial Demand for Product Allocation
Porter Jenkins, Hua Wei, J. Stockton Jenkins +1
Connecting consumers with relevant products is a very important problem in both online and offline commerce. In physical retail, product placement is an effective way to connect co…
Learning Phase Competition for Traffic Signal Control
Guanjie Zheng, Yuanhao Xiong, Xinshi Zang +6
Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions. Among them, improving the urban transportat…
Diagnosing Reinforcement Learning for Traffic Signal Control
Guanjie Zheng, Xinshi Zang, Nan Xu +5
With the increasing availability of traffic data and advance of deep reinforcement learning techniques, there is an emerging trend of employing reinforcement learning (RL) for traf…
CoLight: Learning Network-level Cooperation for Traffic Signal Control
Hua Wei, Nan Xu, Huichu Zhang +7
Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating t…
A Survey on Traffic Signal Control Methods
Hua Wei, Guanjie Zheng, Vikash Gayah +1
Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersecti…
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Huaxiu Yao, Xianfeng Tang, Hua Wei +2
Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For exam…