A multi-task memory network with knowledge adaptation for multimodal demand forecasting. (October 2021)

• Record Type: Journal Article
• Authors: Li, Can; Bai, Lei; Liu, Wei; Yao, Lina; Waller, S. Travis
• Is Part Of: Transportation research. Volume 131(2021)
• Publication Date: 2021-10
• Subjects: Memory neural network -- Multimodal -- Demand prediction -- Knowledge adaptation; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103352
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Co-predict multimodal demand given heterogeneous modes. Enhance station-sparse modes demand prediction with station-intensive modes. Demonstrate the importance of knowledge adaptation for demand prediction. Abstract: Travel demand forecasting is useful for both trip and service planning, and thus is of great importance. Most existing studies focus on demand forecasting for a single mode, while much less attention has been paid to multimodal demand forecasting. This paper develops a multimodal demand forecasting approach, which can learn and utilize information/knowledge from different public transit modes and thus improve the demand prediction of the travel mode with sparse observations (e.g., station-sparse mode). In particular, this study focuses on improving the passenger demand prediction accuracy of the station-sparse mode(s) with the help of the station-intensive mode (i.e., the mode with more sufficient knowledge and intensive station distribution over space). We propose a novel K nowledge A daptation with A ttentive M ulti-task M emory Network (KA2M2 ) in order to utilize closely-related demand patterns from the station-intensive mode for demand forecasting of the station-sparse mode(s). Specifically, we first design a memory-augmented recurrent network for enhancing the ability to capture the long-and-short term demand information and storing the extracted temporal knowledge of each transit mode. Then, we develop and integrate an attention-based knowledge adaptation module to adapt relevant information from the station-intensive source to the station-sparse source(s). The experimental results on a real-world dataset collected from the Greater Sydney area covering four public transport modes (bus, train, light rail, and ferry) demonstrate that the proposed approach consistently outperforms a number of baseline methods and state-of-the-art models. Our findings also illustrate that incorporating information/knowledge from multimodal trip records can enhance the demand forecasting accuracy for station-sparse modes.