A Recommender for Personalized Travel Planning Using Stacked Autoencoder in a Multimodal Transportation Network

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.