Urban Computing

TLDR

Urbanization has modernized lives but also created problems such as traffic congestion, energy use, and pollution, prompting an interdisciplinary field—urban computing—that blends computer science with transportation, engineering, environment, economy, ecology, and sociology. The article introduces urban computing, outlining its general framework and key challenges from a computer‑science perspective, with the aim of addressing urban problems through city‑generated data such as traffic flow, human mobility, and geographic information. Urban computing is built on a recurrent cycle that links urban sensing, data management, analytics, and service provision, and the article classifies its applications into seven categories and its core technologies into four pillars—sensing, data management, knowledge fusion, and visualization—while outlining future research directions.

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.