Route selection can greatly affect vehicle fuel consumption and emissions. Finding the most fuel/energy-efficient route is known as the eco-routing problem. Existing eco-routing solutions do not effectively consider the critical traffic signal information and rely on fuel consumption models that may not be sufficiently accurate. To address the eco-routing problem in a signalized traffic network, this paper proposes a graph convolutional network based multi-objective meta-deep Q-learning (GM2DQL) method. The problem is formulated as dynamic multi-objective Markov decision processes (MOMDP) and is tackled through deep reinforcement learning and meta-learning. We identify that graph convolutional network (GCN) is an efficient and suitable feature representation for a signalized traffic network. GM2DQL can explore the optimal routes with respect to drivers’ different preferences on saving fuel and travel time. Through GM2DQL, the agent is trained under a series of learning environments that are characterized by historical vehicle trajectories, fuel consumption data, and traffic signal data in the remote data center. The vehicle requesting eco-routing service can download the model that represents the action value function of the historical dynamic driving conditions. The model in the vehicle can quickly adapt to the most recent driving condition through online one-shot learning and predict the optimal eco-routes for the subsequent unseen driving conditions of the signalized traffic network. Extensive proof-of-concept experiments validate that GM2DQL can effectively discover optimal eco-routes. It saves up to 71% travel time and 62% fuel, compared to the conventional shortest-path routing strategy that is widely used in navigation systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Graph Convolutional Network Based Multi-Objective Meta-Deep Q-Learning for Eco-Routing


    Contributors:
    Ma, Xin (author) / Xie, Yuanchang (author) / Chigan, Chunxiao (author)


    Publication date :

    2024-07-01


    Size :

    8378535 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Meta-Deep Q-Learning for Eco-Routing

    Ma, Xin / Xie, Yuanchang / Chigan, Chunxiao | IEEE | 2019


    Marine multi-ship encounter graph structured learning method based on graph convolutional neural network

    GAO MIAO / ZHANG ANMIN / HAN YU et al. | European Patent Office | 2024

    Free access

    Multi-objective recognition based on deep learning

    Liu, Xin / Wu, Junhui / Man, Yiyun et al. | Emerald Group Publishing | 2020



    Dynamic multi-graph convolutional network traffic flow prediction method based on graph transformation

    HU YONGLI / PENG TING / GUO KAN et al. | European Patent Office | 2022

    Free access