Urban Computing

Yu Zheng, Licia Capra, Ouri Wolfson, Hai Yang
2014 ACM Transactions on Intelligent Systems and Technology  
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
more » ... , 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. 38:4 Y. Zheng et al. information will be delivered to the transportation authority for dispersing traffic and diagnosing the anomaly. The system continues the loop for an instant and unobtrusive detection of urban anomalies, helping improve people's driving experiences and reduce traffic congestion. Compared with other systems (e.g., web search engines) that are based on a single (modal)-data/single-task framework (i.e., information retrieval from web pages), urban computing holds a multi(modal)-data/multitask framework. The tasks of urban computing include improving urban planning, easing traffic congestion, reducing energy consumption, and reducing air pollution. Additionally, we usually need to harness a diversity of data sources in a single task. For instance, the aforementioned anomaly detection uses human mobility data, road networks, and social media. Different tasks can be fulfilled by combining different data sources with different data acquisition, management, and analytics techniques from different layers of the framework. Key Challenges The goals and framework of urban computing result in three main challenges: 38:6 Y. Zheng et al. challenging. In addition, when facing multiple types and huge volumes of data, seeing how exploratory visualization [Andrienko et al. 2003] can provide an interactive way for people to generate new hypotheses becomes even more difficult. This calls for an integration of instant data-mining techniques into a visualization framework, which is still missing in urban computing. 3. Hybrid systems blending the physical and virtual worlds: Unlike a search engine or a digital game where the data was generated and consumed in the digital world, urban computing usually integrates the data from both worlds (e.g., combining traffic with social media). Alternatively, the data (e.g., GPS trajectories of vehicles) is generated in the physical world and then sent back to the digital world, such as a cloud system. After the data is processed with other data sources in the cloud, the knowledge learned from the data will be used to serve users from the physical world via mobile clients (e.g., driving direction suggestions, taxi ridesharing, and air quality monitoring). The design of such a system is much more challenging than for conventional systems that only reside in one world, as the system needs to communicate with many devices and users simultaneously and send and receive data of different formats and at different frequencies. Urban Data
doi:10.1145/2629592 fatcat:no5gcshbmrdfphv6ewm6wdoewq