Region representation learning via mobility flow
Increasing amount of urban data are being accumulated and released to public; this enables
us to study the urban dynamics and address urban issues such as crime, traffic, and quality …
us to study the urban dynamics and address urban issues such as crime, traffic, and quality …
[PDF][PDF] Representing urban functions through zone embedding with human mobility patterns
Urban functions refer to the purposes of land use in cities where each zone plays a distinct
role and cooperates with each other to serve people's various life needs. Understanding …
role and cooperates with each other to serve people's various life needs. Understanding …
[PDF][PDF] Multi-view joint graph representation learning for urban region embedding
The increasing amount of urban data enables us to investigate urban dynamics, assist urban
planning, and, eventually, make our cities more livable and sustainable. In this paper, we …
planning, and, eventually, make our cities more livable and sustainable. In this paper, we …
City-wide traffic volume inference with loop detector data and taxi trajectories
The traffic volume on road segments is a vital property of the transportation efficiency. City-
wide traffic volume information can benefit people with their everyday life, and help the …
wide traffic volume information can benefit people with their everyday life, and help the …
Interpreting traffic dynamics using ubiquitous urban data
Given a large collection of urban datasets, how can we find their hidden correlations? For
example, New York City (NYC) provides open access to taxi data from year 2012 to 2015 …
example, New York City (NYC) provides open access to taxi data from year 2012 to 2015 …
Representing urban forms: A collective learning model with heterogeneous human mobility data
Human mobility data refers to records of human movements, such as cellphone traces,
vehicle GPS trajectories, geo-tagged posts, and photos. While successfully mining human …
vehicle GPS trajectories, geo-tagged posts, and photos. While successfully mining human …
Urban function classification at road segment level using taxi trajectory data: A graph convolutional neural network approach
Extracting hidden information from human mobility patterns is one of the long-standing
challenges of urban studies. In addition, exploring the relationship between urban functional …
challenges of urban studies. In addition, exploring the relationship between urban functional …
Learning urban community structures: A collective embedding perspective with periodic spatial-temporal mobility graphs
Learning urban community structures refers to the efforts of quantifying, summarizing, and
representing an urban community's (i) static structures, eg, Point-Of-Interests (POIs) …
representing an urban community's (i) static structures, eg, Point-Of-Interests (POIs) …
Dynamic multi-view graph neural networks for citywide traffic inference
Accurate citywide traffic inference is critical for improving intelligent transportation systems
with smart city applications. However, this task is very challenging given the limited training …
with smart city applications. However, this task is very challenging given the limited training …
Revealing latent characteristics of mobility networks with coarse-graining
Previous theoretical and data-driven studies on urban mobility uncovered the repeating
patterns in individual and collective human behavior. This paper analyzes the travel demand …
patterns in individual and collective human behavior. This paper analyzes the travel demand …