A Recommender for Personalized Travel Planning Using Stacked Autoencoder in a Multimodal Transportation Network
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 2
Abstract
This paper proposes a recommender in multimodal transportation-as-a-service (MMTaaS) system that offers personalized travel planning in multimodal transportation network. The framework focuses on three key aspects: (1) multimodal path-planning based on individual travel demands within a large-scale road network; (2) determination of traveler-specific travel itineraries, taking into account various information sources such as the topology of the road network and the supply of each transportation mode; and (3) personalization of travel plan recommendations using stacked autoencoder based on individual traveler attributes. The proposed recommender adopts a data and model-driven approach, leveraging data from various sources to inform decision-making and model the problem. The effectiveness and feasibility of the MMTaaS framework are demonstrated through a case study in Jiaxing City, Zhejiang Province, China, which highlights the framework’s ability to handle single and multimodal traffic trips and customized individual trips. The results of this study demonstrate the effectiveness and feasibility of the proposed MMTaaS recommender and provide valuable insights for the development of future transportation-as-a-service systems.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study is supported by the Key Project (No. 52131203) and the Youth Program (No. 52102375) of the National Natural Science Foundation of China, the Youth Program (No. BK20210247) of the Natural Science Foundation of Jiangsu Province, China, and the Fundamental Research Funds for the Central Universities, China (No. 2242022R40025).
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 2 • February 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: May 4, 2023
Accepted: Jun 26, 2023
Published online: Nov 24, 2023
Published in print: Feb 1, 2024
Discussion open until: Apr 24, 2024
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