research-article

DeepMeshCity: A Deep Learning Model for Urban Grid Prediction

Authors:

Xiucheng Li, and

Gao CongAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 6

Article No.: 148, Pages 1 - 26

https://doi.org/10.1145/3652859

Published: 29 April 2024 Publication History

Abstract

Urban grid prediction can be applied to many classic spatial-temporal prediction tasks such as air quality prediction, crowd density prediction, and traffic flow prediction, which is of great importance to smart city building. In light of its practical values, many methods have been developed for it and have achieved promising results. Despite their successes, two main challenges remain open: (a) how to well capture the global dependencies and (b) how to effectively model the multi-scale spatial-temporal correlations? To address these two challenges, we propose a novel method—DeepMeshCity, with a carefully-designed Self-Attention Citywide Grid Learner (SA-CGL) block comprising of a self-attention unit and a Citywide Grid Learner (CGL) unit. The self-attention block aims to capture the global spatial dependencies, and the CGL unit is responsible for learning the spatial-temporal correlations. In particular, a multi-scale memory unit is proposed to traverse all stacked SA-CGL blocks along a zigzag path to capture the multi-scale spatial-temporal correlations. In addition, we propose to initialize the single-scale memory units and the multi-scale memory units by using the corresponding ones in the previous fragment stack, so as to speed up the model training. We evaluate the performance of our proposed model by comparing with several state-of-the-art methods on four real-world datasets for two urban grid prediction applications. The experimental results verify the superiority of DeepMeshCity over the existing ones. The code is available at https://github.com/ILoveStudying/DeepMeshCity.

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Cited By

Duong HPham HTruong TFournier-Viger P(2024)Efficient algorithms to mine concise representations of frequent high utility occupancy patternsApplied Intelligence10.1007/s10489-024-05296-254:5(4012-4042)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1007/s10489-024-05296-2

Index Terms

DeepMeshCity: A Deep Learning Model for Urban Grid Prediction
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 6

July 2024

760 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3613684

Editor:
Jian Pei
Duke University, USA

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Publication History

Published: 29 April 2024

Online AM: 15 March 2024

Accepted: 02 March 2024

Revised: 05 February 2024

Received: 23 September 2023

Published in TKDD Volume 18, Issue 6

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National Natural Science Foundation of China
Shenzhen College Stability Support Plan
China Scholarship Council
National Research Foundation, Singapore under its AI Singapore Programme

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Duong HPham HTruong TFournier-Viger P(2024)Efficient algorithms to mine concise representations of frequent high utility occupancy patternsApplied Intelligence10.1007/s10489-024-05296-254:5(4012-4042)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1007/s10489-024-05296-2

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