[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Han et al., 2023 - Google Patents

Congestion control of vehicle traffic networks by learning structural and temporal patterns

Han et al., 2023

View PDF
Document ID
136130743051761409
Author
Han S
Chung S
Gustafson J
Publication year
Publication venue
Learning for Dynamics and Control Conference

External Links

Snippet

For many network control problems, there exist natural spatial structures and temporal repetition in the network that can be exploited so that controller synthesis does not spend unnecessary time and energy redundantly computing control laws. One notable example of …
Continue reading at proceedings.mlr.press (PDF) (other versions)

Classifications

    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/012Measuring and analyzing of parameters relative to traffic conditions based on the source of data from other sources than vehicle or roadside beacons, e.g. mobile networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/07Controlling traffic signals
    • G08G1/08Controlling traffic signals according to detected number or speed of vehicles

Similar Documents

Publication Publication Date Title
Wang et al. Adaptive Traffic Signal Control for large-scale scenario with Cooperative Group-based Multi-agent reinforcement learning
CN109754605B (en) Traffic prediction method based on attention temporal graph convolution network
Ouallane et al. Fusion of engineering insights and emerging trends: Intelligent urban traffic management system
Jin et al. A group-based traffic signal control with adaptive learning ability
CN109272157A (en) A kind of freeway traffic flow parameter prediction method and system based on gate neural network
US11783702B2 (en) Method and system for adaptive cycle-level traffic signal control
CN113316808A (en) Spatial control of traffic signals by space-time expansion of traffic conditions
Brys et al. Distributed learning and multi-objectivity in traffic light control
Mo et al. CVLight: Decentralized learning for adaptive traffic signal control with connected vehicles
KR20200084750A (en) Traffic speed prediction using a deep neural network to accommodate citywide spatio-temporal correlations
Egea et al. Assessment of reward functions for reinforcement learning traffic signal control under real-world limitations
Zheng et al. A novel approach to coordinating green wave system with adaptation evolutionary strategy
Mukhtar et al. CCGN: Centralized collaborative graphical transformer multi-agent reinforcement learning for multi-intersection signal free-corridor
EL KHAILI et al. Towards a Green Supply Chain Based on Smart Urban Traffic Using Deep Learning Approach
Han et al. Congestion control of vehicle traffic networks by learning structural and temporal patterns
Jalel et al. Continuous time markov chain traffic model for urban environments
Withanawasam et al. Multi-agent based road traffic control optimization
Soleimany et al. Hierarchical federated learning model for traffic light management in future smart
CN111325414A (en) Parallel traffic prediction method based on data driving
Kumar et al. Deep reinforcement learning with vehicle heterogeneity based traffic light control for intelligent transportation system
CN112258856A (en) Method for establishing regional traffic signal data drive control model
Abhishek et al. Smart traffic light controller using deep reinforcement learning
Hassan et al. Collaborative Traffic Signal Automation Using Deep Q-Learning
Nae et al. Fuzzy-logic adaptive control of traffic in an urban junction
Xu et al. Stochastic traffic modelling and decentralised signal control based on a state transition probability model