Urban Computing: Concepts, Methodologies, and Applications

By Yu Zheng, Licia Capra, Ouri Wolfson, Hai Yang. 2014

1,690 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

In Transportation

In Signal Processing

Citations in the corpus

coMobile: real-time human mobility modeling at urban scale using multi-view learning (2015, background): “Nowadays, we are in a rapid process of urbanization where more than half of people in the world has moved to urban areas [20].”
Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data (2017, methodology): “…other trafﬁc related information including weather, social events, and road features, we utilize the context-aware matrix factorization schema, which can effectively integrate multiple related matrices to improve the performance of matrix factorization [Karatzoglou et al. 2010; Zheng et al. 2014b].”
CityTransfer (2018, methodology): “Recently, the proliferation of big data in cities has fostered unprecedented opportunities to deliver intelligent urban services by leveraging data mining and machine learning techniques [14, 42].”
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey (2023, background): “According to urban computing theories [1], predictive learning based on massive urban data is the most important loop, forming the foundation for intelligent decision-making, scheduling, and management in smart cities.”
Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs (2020, background): “A typical IIoT system may involve categories of industrial data, ranging from the trafﬁc records originated from the freight systems or regular crowds [3], to the sophisticated production data from the pipelines in factories and supplement chains.”
Citywide traffic congestion estimation with social media (2015, background): “For example, as deploying and maintaining loop sensors is very expensive in terms of money and human resources, they are usually employed for major roads rather than low-level streets [24].”
Large-scale vehicle re-identification in urban surveillance videos (2016, background): “Vehicle Re-Id can play great roles in intelligent transportation [8], urban computing [9], and intelligent surveillance [10], which can quickly discover, locate, and track the target vehicles.”
Trajectory Data Mining (2015, background): “Such trajectories offer us unprecedented information to understand moving objects and locations, fostering a broad range of applications in location-based social networks [140], intelligent transportation systems, and urban computing [142].”
DeepCity: A Feature Learning Framework for Mining Location Check-Ins (2016, background): “All of these open up an emerging field, namely urban informatics or urban computing (Zheng et al. 2014).”
Tackling Climate Change with Machine Learning (2019, background): “here is a growing volume of data coming from soft sensing, due to the widespread adoption of personal devices like smartphones that can provide movement data and geotagged pictures.20 Urban computing [330] is an emerging ﬁeld looking at data analytics in urban spaces, and aiming to yield insights fo”
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, background): “Urban planning Improving experience-based decisions on urbanization issues, such as street networks design and infrastructure maintenance [30], [161], [162]”
A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance (2016, background): “pervasive applications in video surveillance [4], intelligent transportation [5], and urban computing [6], which can quickly discover, locate, and track the target vehicles in large-scale surveillance videos.”
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017, background): “Predicting crowd ﬂows in a city is of great importance to tra ﬃ c management, risk assessment, and public safety [2].”
PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance (2018, background): “Vehicle Re-Id can be pervasively applied in intelligent surveillance systems [5], smart transportation [6], and urban computing [7].”
A Survey on Deep Learning for Human Mobility (2020, background): “It is a crucial problem given its impact on several aspects of society, from public safety [237] to the definition of on-demand services [240], the management of land use [85], and traffic optimization [237].”
Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data (2014, background): “…can be seen as sensors of aggregated human activity [14, 23] and have been used to monitor citizens’ mobility patterns and urban interactions [21, 48], to understand individual spending behaviors [33], to infer people’s traits [13, 37] and states [3], to map and model the spreading of diseases…”