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Multi-step Point-of-Interest-level Crowd Flow Prediction Based on Meta Learning

Published: 21 June 2022 Publication History

Abstract

Point-of-Interest-level(POI) crowd flow prediction is an important task for businesses and consumers. Based on POI-level crowd flow prediction, businesses could make more reasonable business arrangements, and consumers could make more suitable travel plans. However, POI-level crowd flow prediction is a challenging task for two aspects: 1) Compared with region-level crowd flow, the area of POI is smaller and the fluctuation of POI-level crowd flow is greater; 2) There are diverse temporal correlations of different POIs and varies over time. To tackle the above challenges, following the antoencoder architecture, we propose a multi-step POI-level crowd flow prediction model(Ms-PLCFP) to predict the crowd flow at all POIs at once. A meta learner is used to obtain meta knowledge from POI category, POI popularity, etc. Then meta-RNN+ is applied to model the relations between temporal correlations and meta knowledge so as to capture diverse temporal correlations. Furthermore, a multi-scale temporal attention mechanism which contains multiple different scales of temporal attention is employed to smooth input crowd flow at lower level and capture global dependencies of input crowd flow at higher level. We evaluated Ms-PLCFP on two real-world datasets and Ms-PLCFP achieved significant improvements over the baselines, which shows the effectiveness of our model.

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ICMLC '22: Proceedings of the 2022 14th International Conference on Machine Learning and Computing
February 2022
570 pages
ISBN:9781450395700
DOI:10.1145/3529836
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2022

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Author Tags

  1. Meta Learning
  2. POI-level Crowd Flow Prediction
  3. Time Series Prediction

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  • Research-article
  • Research
  • Refereed limited

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  • The National Key R&D Program of China

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ICMLC 2022

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