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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Urban Computing: Concepts, Methodologies, and Applications

GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. We build our system based on a real-world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.

/pdf/t-drive-driving-directions-based-on-taxi-trajectories-1xb3zuw0ip.pdf

T-drive: driving directions based on taxi trajectories

Rapid urbanization creates new challenges and issues, and the smart city concept offers opportunities to rise to these challenges, solve urban problems and provide citizens with a better living environment. This paper presents an exhaustive literature survey of smart cities. First, it introduces the origin and main issues facing the smart city concept, and then presents the fundamentals of a smart city by analyzing its definition and application domains. Second, a data-centric view of smart city architectures and key enabling technologies is provided. Finally, a survey of recent smart city research is presented. This paper provides a reference to researchers who intend to contribute to smart city research and implementation.

A literature survey on smart cities

This paper presents a smart driving direction system leveraging the intelligence of experienced drivers. In this system, GPS-equipped taxis are employed as mobile sensors probing the traffic rhythm of a city and taxi drivers' intelligence in choosing driving directions in the physical world. We propose a time-dependent landmark graph to model the dynamic traffic pattern as well as the intelligence of experienced drivers so as to provide a user with the practically fastest route to a given destination at a given departure time. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest and customized route for end users. We build our system based on a real-world trajectory data set generated by over 33,000 taxis in a period of three months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70 percent of the routes suggested by our method are faster than the competing methods, and 20 percent of the routes share the same results. On average, 50 percent of our routes are at least 20 percent faster than the competing approaches.

/pdf/t-drive-enhancing-driving-directions-with-taxi-drivers-5hlw607bzt.pdf

T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence

With rapid population growth, urban designers face tremendous challenges to accommodate the increasing size of the population in urban areas while simultaneously considering future environmental, social, and economic impacts. A “smart city” is an urban development vision that integrates multiple information and communication technologies to manage the assets of a city, including its information systems, transportation systems, power plants, water supply networks, waste management systems, and other community services provided by a local department. The goal of creating a smart city is to improve the quality of life of citizens by using technology and by addressing the environmental, social, cultural, and physical needs of a society. Data modeling and data visualization are integral parts of planning a smart city, and planning professionals currently seek new methods for real-time simulations. The impact analysis of “what-if scenarios” frequently takes a significant amount of time and resources, and virtual reality (VR) is a potential tool for addressing these challenges. VR is a computer technology that replicates an environment, whether real or imagined, and simulates the physical presence and environment of a user to allow for user interaction. This paper presents a review of the capacity of VR to address current challenges in creating, modeling, and visualizing smart cities through material modeling and light simulation in a VR environment. This study can assist urban planners, stakeholders, and communities to further understand the roles of planning policies in creating a smart city, particularly in the early design stages. The significant roles of technologies, such as VR, in targeting real-time simulations and visualization requirements for smart cities are emphasized.

/pdf/investigating-the-role-of-virtual-reality-in-planning-for-3kt0y92vhk.pdf

Investigating the Role of Virtual Reality in Planning for Sustainable Smart Cities

The application domain of intelligent transportation is plagued by a shortage of data sources that adequately assess traffic situations. Typically, to provide routing and navigation solutions map attributes in the form of static weights as derived from road categories and speed limits used for road networks. With the advent of Floating Car Data (FCD) and specifically the GPS-based tracking data component, a means was found to derive accurate and up-to-date travel times, i.e., qualitative traffic information. FCD is a by-product in fleet management applications and given a minimum number and uniform distribution of vehicles, this data can be used for accurate traffic assessment and also prediction. This work showcases a system that facilitates the collection of FCD, produces dynamic travel time information, and provides value-added services based on the dynamic travel times. The essential components that will be discussed are a Web-services-based data collection approach, sophisticated map-matching algorithms, a data management architecture and an online visualization platform.

https://publik.tuwien.ac.at/files/PubDat_168982.pdf

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Papers

Dynamic travel time provision for road networks