LAUSR

Abstract Efficient multimodal public transport has been recognized as an effective solution for realizing sustainable intercity transport. However, it has not been well accepted by the general public due to the poor coordination among transport modes, inconvenience caused by transfers, difficulties on route plannings, etc. To address the problem and facilitate the popularity of multimodal intercity travel, this research proposed the design of personalized multimodal travel service based on SPSS (Smart Product Service System). It not only could provide multimodal route recommendations based on individual preferences, but also could improve its performance along usage. Meanwhile, the influencing factors of multimodal intercity travel is investigated, based on which the travel choice model is developed. Besides, a few-short learning method was proposed, which could mine the individual preference even from limited historical travel data. Finally, an experimental case study was conducted that demonstrates the effectiveness of the proposed solution.