This paper considers the use of deep learning models to enhance optimization algorithms for transit network design. Transit network design is the problem of determining routes for transit vehicles that minimize travel time and operating costs, while achieving full service coverage. State-of-the-art meta-heuristic search algorithms give good results on this problem, but can be very time-consuming. In contrast, neural networks can learn sub-optimal but fast-to-compute heuristics based on large amounts of data. Combining these approaches, we develop a fast graph neural network model for transit planning, and use it to initialize state-of-the-art search algorithms. We show that this combination can improve the results of these algorithms on a variety of metrics by up to 17%, without increasing their run time; or they can match the quality of the original algorithms while reducing the computing time by up to a factor of 50.


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    Title :

    Augmenting Transit Network Design Algorithms with Deep Learning


    Contributors:


    Publication date :

    2023-09-24


    Size :

    275877 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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