2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLSP-47.6
Paper Title PROBABILISTIC GRAPH NEURAL NETWORKS FOR TRAFFIC SIGNAL CONTROL
Authors Ting Zhong, Zheyang Xu, Fan Zhou, University of Electronic Science and Technology of China, China
SessionMLSP-47: Applications of Machine Learning
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-APPL] Applications of machine learning
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Intelligent traffic signal control is crucial for efficient transportation systems. Recent studies use reinforcement learning (RL) to coordinate traffic signals and improve traffic signal cooperation. However, they either design the state of agents in a heuristic manner or model traffic dynamics in a deterministic way. This work presents a variational graph learn- ing model TSC-GNN (Traffic Signal Control via probabilistic Graph Neural Networks) to learn the latent representations of agents and generate Q-value while taking traffic uncertainty conditions into account. Besides, we explain the rationality behind our state design using transportation theory. Experimental results conducted on real-world datasets demonstrate our model’s superiority, e.g., it achieves more than 8% traffic efficiency improvement compared with the state-of-the-art baselines.