THESIS
2022
1 online resource (xiii, 138 pages) : illustrations (some color)
Abstract
Urban events are the sets of real-world occurrences in urban space associated with specific
topics. Discovering, understanding, and forecasting various urban events are essential to
facilitate smart-city applications and benefit urban life. In recent years, mobile devices
and sensors widely located in cities have collected large amounts of data produced in urban
space, which form a large-scale, cross-domain, and multi-view data ecosystem. The
collected data, termed urban big data, provide an unprecedented opportunity to discover
and analyze urban events. In this thesis, we develop data-driven methodologies for urban
events mining. We present four of our works on urban anomaly detection, urban location
representation learning, road network traffic prediction, and urban events prediction,...[
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Urban events are the sets of real-world occurrences in urban space associated with specific
topics. Discovering, understanding, and forecasting various urban events are essential to
facilitate smart-city applications and benefit urban life. In recent years, mobile devices
and sensors widely located in cities have collected large amounts of data produced in urban
space, which form a large-scale, cross-domain, and multi-view data ecosystem. The
collected data, termed urban big data, provide an unprecedented opportunity to discover
and analyze urban events. In this thesis, we develop data-driven methodologies for urban
events mining. We present four of our works on urban anomaly detection, urban location
representation learning, road network traffic prediction, and urban events prediction, respectively.
First, we introduce an urban dynamic decomposition method to detect urban anomalies.
We propose a neural network model to estimate normal urban dynamics using spatiotemporal
features and detect abnormal events via residual analysis. We validate the
effectiveness of the proposed method with both synthetic and real-world event datasets.
Regions are the primary spatial units for urban event analysis. The second work focuses on learning an embedding space from urban big data for urban regions. We propose to
construct multi-view relations among urban regions and learn urban region embeddings
through a multi-view joint learning model. Experiments on real-world datasets prove the
effectiveness of the learned embeddings on downstream applications, including predicting
long-term regional event statistics such as crime rates. Traffic prediction is a crucial
task for congestion events diagnosis and control. In the third work, we propose a traffic
prediction model called Adaptive Spatiotemporal Convolution Network, which models
complicated spatiotemporal traffic dependencies adaptively with awareness of the real-time
traffic conditions. Experiments on real-world traffic datasets from California and Beijing
demonstrate that the proposed model outperforms the state-of-the-art. In the fourth
work, we target at location and arrival time prediction of individual urban anomalous
events. We design a message passing based mechanism to model the spatiotemporal impacts
of historical events. We make joint predictions of event locations and times based on
regional states and environmental factors. Our method achieves superior performances
on two real-world urban events datasets compared with existing spatiotemporal prediction
methods.
In the end, we conclude this thesis with future challenges and research directions related
to data-driven urban events analysis.
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