A transit network design and frequency setting model with graph neural network and deep reinforcement learning

Junjun Li; Hao Dong; Xuedong Zhao; Hao Tang; Aimin Yin; Ruchen Xue

doi:10.1117/12.3003828

11 October 2023 A transit network design and frequency setting model with graph neural network and deep reinforcement learning

Junjun Li, Hao Dong, Xuedong Zhao, Hao Tang, Aimin Yin, Ruchen Xue

Author Affiliations +

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128005Y (2023) https://doi.org/10.1117/12.3003828
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

The urban public transportation system is an essential element of our cities and requires efficient transit network design to provide high-quality service to passengers. The quality of the transit network directly impacts the directness of passengers, as well as the profitability of the transportation company. The purpose of our paper is to introduce a novel method for addressing the challenges associated with the Transit Network Design and Frequency Setting Problem (TNDFSP). A number of optimization techniques have been proposed for TNDFSP, with previous approaches often relying on a sequential optimization approach that tackles transit network design and service frequency setting as separate tasks. In contrast, our new algorithm takes a simultaneous optimization approach leveraging graph neural networks and deep reinforcement learning to optimize passenger benefits, operating costs, and service frequencies. We test the proposed algorithm on the popular Mandl Swiss network and produce highly competitive results.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junjun Li, Hao Dong, Xuedong Zhao, Hao Tang, Aimin Yin, and Ruchen Xue "A transit network design and frequency setting model with graph neural network and deep reinforcement learning", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128005Y (11 October 2023); https://doi.org/10.1117/12.3003828

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KEYWORDS

Neural networks

Design and modelling

Network architectures

Matrices

Education and training

Transportation

Computer programming

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