research-article

Urban Computing: Concepts, Methodologies, and Applications

Authors:

Hai YangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 3

Article No.: 38, Pages 1 - 55

https://doi.org/10.1145/2629592

Published: 18 September 2014 Publication History

Abstract

Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 3

Special Section on Urban Computing

September 2014

361 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2648782

Editor:
Qiang Yang
Hong Kong University of Science and Technology, Hong Kong

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Copyright © 2014 ACM.

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Publication History

Published: 18 September 2014

Revised: 01 April 2014

Accepted: 01 March 2014

Received: 01 January 2014

Published in TIST Volume 5, Issue 3

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Meesala YUma Mahesh AReddy Bellana AObulesu B(2024)Air Quality Index Prediction using Bi-LSTMInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24APR2035(1185-1192)Online publication date: 29-Apr-2024
https://doi.org/10.38124/ijisrt/IJISRT24APR2035
Chen JYang GZhang ZWang W(2024)ST-D3DDARN: Urban traffic flow prediction based on spatio-temporal decoupled 3D DenseNet with attention ResNetPLOS ONE10.1371/journal.pone.030542419:6(e0305424)Online publication date: 12-Jun-2024
https://doi.org/10.1371/journal.pone.0305424
Rong CDing JLi Y(2024)An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and TechniquesACM Computing Surveys10.1145/3682058Online publication date: 26-Jul-2024
https://doi.org/10.1145/3682058
Xu XZhong TYu HZhou FTrajcevski G(2024)Overcoming Catastrophic Forgetting in Continual Fine-Grained Urban Flow InferenceACM Transactions on Spatial Algorithms and Systems10.1145/3660523Online publication date: 20-Apr-2024
https://doi.org/10.1145/3660523
Mokbel MSakr MXiong LZüfle AAlmeida JAnderson TAref WAndrienko GAndrienko NCao YChawla SCheng RChrysanthis PFei XGhinita GGraser AGunopulos DJensen CKIM JKim KKröger PKrumm JLauer JMagdy ANascimento MRavada SRenz MSacharidis DSalim FSarwat MSchoemans MShahabi CSpeckmann BTanin ETeng XTheodoridis YTorp KTrajcevski Gvan Kreveld MWenk CWerner MWong RWU SXu JYoussef MZeinalipour DZhang MZimányi E(2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/3652158Online publication date: 7-May-2024
https://doi.org/10.1145/3652158
Jiang WHan JLiu HTao TTan NXiong HBaeza-Yates RBonchi F(2024)Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion PredictionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671507(5206-5217)Online publication date: 25-Aug-2024
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Liu YBattersby SDodge S(2024)Scaling up time–geographic computation for movement interaction analysisTransactions in GIS10.1111/tgis.13205Online publication date: 26-Jun-2024
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Coscia ASuh AChang REndert A(2024)Preliminary Guidelines for Combining Data Integration and Visual Data AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.333451330:10(6678-6690)Online publication date: 1-Oct-2024
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