The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Urban Computing: Concepts, Methodologies, and Applications
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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Cites
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An online algorithm for segmenting time series
Keogh, E., Chu, S., Hart, D., & Pazzani, M. (2002) · Proceedings 2001 IEEE International Conference on Data Mining · doi:10.1109/icdm.2001.989531 · Cited by 1,149
Understanding mobility based on GPS data
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Self-citationLearning transportation mode from raw gps data for geographic applications on the web
Zheng, Y., Liu, L., Wang, L., & Xie, X. (2008) · Proceedings of the 17th international conference on World Wide Web · doi:10.1145/1367497.1367532 · Cited by 616
Self-citationWatkins, K. E., Ferris, B., Borning, A., Rutherford, G. S., & Layton, D. (2011) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2011.06.010 · Cited by 384
Field trial of Tiramisu: crowd-sourcing bus arrival times to spur co-design
Zimmerman, J., Tomasic, A., Garrod, C., Yoo, D., Hiruncharoenvate, C., Aziz, R., Thiruvengadam, N. R., Huang, Y., & Steinfeld, A. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979187 · Cited by 150 · cited 1×
“This can be, for example, GPS data from a user s mobile phone, as already explored in the Tiramisu project [Zimmerman et al. 2011], which is then used to estimate real-time bus arrivals.”
Method
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Smart sustainable cities of the future: An extensive interdisciplinary literature review
Bibri, S. E., & Krogstie, J. (2017) · Sustainable Cities and Society · doi:10.1016/j.scs.2017.02.016 · Cited by 1,599
A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance
Liu, X., Liu, W., Mei, T., & Ma, H. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_53 · Cited by 641
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2914030 · Cited by 594
Large-scale vehicle re-identification in urban surveillance videos
Liu, X., Liu, W., Ma, H., & Fu, H. (2016) · 2016 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2016.7553002 · Cited by 513
Methodologies for Cross-Domain Data Fusion: An Overview
Zheng, Y. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2465959 · Cited by 454
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Metadata
- Authors
- Zheng, Y., Capra, L., Wolfson, O., & Yang, H.
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2014-09-18
- Volume
- 5
- Issue
- 3
- Pages
- 1-55
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W2112738128
- Cited by
- 1,507
- References
- 6
- FWCI
- 170.71
- Type
- journal article
- Language
- en
- Open access
- closed
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