Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network

• URL: http://arxiv.org/abs/2009.05777v1
• Date: Sat, 12 Sep 2020 12:21:09 GMT
• Title: Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network
• Authors: Can Li, Lei Bai, Wei Liu, Lina Yao, S Travis Waller
• Abstract summary: We propose to enhance the demand prediction of station-sparse modes with the data from station-intensive mode and design aMemory-Augmented Multi-taskRecurrent Network (MATURE) Specifically,MATUREcomprises three components: 1) a memory-augmentedrecurrent network for strengthening the ability to capture the long-short term information and storing temporal knowledge of eachtransit mode; 2) a knowledge adaption module to adapt the knowledge from a station-intensive source to station-sparsesources; and 3) a multi-task learning framework to incorporate all theinformation and forecast the demand of multiple modes jointly.
• Score: 43.356096302298056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate demand forecasting of different public transport modes(e.g., buses and light rails) is essential for public service operation.However, the development level of various modes often varies sig-nificantly, which makes it hard to predict the demand of the modeswith insufficient knowledge and sparse station distribution (i.e.,station-sparse mode). Intuitively, different public transit modes mayexhibit shared demand patterns temporally and spatially in a city.As such, we propose to enhance the demand prediction of station-sparse modes with the data from station-intensive mode and designaMemory-Augmented Multi-taskRecurrent Network (MATURE)to derive the transferable demand patterns from each mode andboost the prediction of station-sparse modes through adaptingthe relevant patterns from the station-intensive mode. Specifically,MATUREcomprises three components: 1) a memory-augmentedrecurrent network for strengthening the ability to capture the long-short term information and storing temporal knowledge of eachtransit mode; 2) a knowledge adaption module to adapt the rele-vant knowledge from a station-intensive source to station-sparsesources; 3) a multi-task learning framework to incorporate all theinformation and forecast the demand of multiple modes jointly.The experimental results on a real-world dataset covering four pub-lic transport modes demonstrate that our model can promote thedemand forecasting performance for the station-sparse modes.

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