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Journal ArticleDOI

Urban Computing: Concepts, Methodologies, and Applications

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TLDR
The concept of urban computing is introduced, discussing its general framework and key challenges from the perspective of computer sciences, and the typical technologies that are needed in urban computing are summarized into four folds.
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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Trajectory Data Mining: An Overview

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Proceedings Article

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Smart sustainable cities of the future: An extensive interdisciplinary literature review

TL;DR: The applied theoretical inquiry into smart sustainable cities of the future is deemed of high pertinence and importance—given that the research in the field is still in its early stages, and that the subject matter draws upon contemporary and influential theories with practical applications.
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Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

TL;DR: Zhang et al. as mentioned in this paper proposed a deep learning-based approach, called ST-ResNet, to collectively forecast the in-flow and out-flow of crowds in each and every region through a city.
Journal ArticleDOI

Social Sensing: A New Approach to Understanding Our Socioeconomic Environments

TL;DR: In this article, the authors use the term social sensing for individual-level big geospatial data and the associat- tation of the data to understand the socioeconomic environments.
References
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Journal ArticleDOI

A Survey on Transfer Learning

TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Journal ArticleDOI

Anomaly detection: A survey

TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Journal ArticleDOI

Tensor Decompositions and Applications

TL;DR: This survey provides an overview of higher-order tensor decompositions, their applications, and available software.
Journal ArticleDOI

Understanding individual human mobility patterns

TL;DR: In this article, the authors study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period and find that the individual travel patterns collapse into a single spatial probability distribution, indicating that humans follow simple reproducible patterns.
Journal ArticleDOI

Using collaborative filtering to weave an information tapestry

TL;DR: Tapestry is intended to handle any incoming stream of electronic documents and serves both as a mail filter and repository; its components are the indexer, document store, annotation store, filterer, little box, remailer, appraiser and reader/browser.
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