A personalized recommendation system for multi-modal transportation systems
Artikel i vetenskaplig tidskrift, 2022

Recommendation system has recently experienced widespread applications in fields like advertising and streaming platforms. Its ability of extracting valuable information from complex data makes it a promising tool for multi-modal transportation system. In this paper, we propose a conceptual framework for proactive travel mode recommendation combining recommendation system and transportation engineering. The proposed framework works by learning from historical user behavioral preferences and ranking the candidate travel modes. In this framework, an incremental scanning method with multiple time windows is designed to acquire multi-scale features from user behaviors. In addition, to alleviate the computational burden brought by the large data size, a hierarchical behavior structure is developed. To further allow for social benefits, the proposed framework proposes to adjust the candidate modes according to real-time traffic states, which is potential in promoting the use of public transport, alleviating traffic congestion, and reducing environmental pollution.

Recommendation system

Multi-modal transportation

Machine learning


Fanyou Wu

Purdue University

Cheng Lyu

Technische Universität München

Yang Liu


Multimodal Transportation

27725871 (ISSN) 27725863 (eISSN)

Vol. 1 2 100016

ATEM - Accelerating transport electrification by machine learning

Europeiska kommissionen (EU) (EC/H2020/101025896), 2021-08-01 -- 2023-08-31.


Transportteknik och logistik

Datavetenskap (datalogi)



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