[HTML][HTML] A survey of urban visual analytics: Advances and future directions
Developing effective visual analytics systems demands care in characterization of domain
problems and integration of visualization techniques and computational models. Urban …
problems and integration of visualization techniques and computational models. Urban …
[PDF][PDF] Geoman: Multi-level attention networks for geo-sensory time series prediction.
Numerous sensors have been deployed in different geospatial locations to continuously and
cooperatively monitor the surrounding environment, such as the air quality. These sensors …
cooperatively monitor the surrounding environment, such as the air quality. These sensors …
A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction
Prediction of individual mobility is crucial in human mobility related applications. Whereas,
existing research on individual mobility prediction mainly focuses on next location prediction …
existing research on individual mobility prediction mainly focuses on next location prediction …
Fine-grained urban flow prediction
Urban flow prediction benefits smart cities in many aspects, such as traffic management and
risk assessment. However, a critical prerequisite for these benefits is having fine-grained …
risk assessment. However, a critical prerequisite for these benefits is having fine-grained …
Traffic congestion prediction by spatiotemporal propagation patterns
Accurate prediction of traffic congestion at the granularity of road segment is important for
planning travel routes and optimizing traffic control in urban areas. Previous works often …
planning travel routes and optimizing traffic control in urban areas. Previous works often …
Spatio-temporal adaptive pricing for balancing mobility-on-demand networks
Pricing in mobility-on-demand (MOD) networks, such as Uber, Lyft, and connected taxicabs,
is done adaptively by leveraging the price responsiveness of drivers (supplies) and …
is done adaptively by leveraging the price responsiveness of drivers (supplies) and …
[PDF][PDF] Modeling Trajectories with Neural Ordinary Differential Equations.
Recent advances in location-acquisition techniques have generated massive spatial
trajectory data. Recurrent Neural Networks (RNNs) are modern tools for modeling such …
trajectory data. Recurrent Neural Networks (RNNs) are modern tools for modeling such …
Efficient metropolitan traffic prediction based on graph recurrent neural network
Traffic prediction is a fundamental and vital task in Intelligence Transportation System (ITS),
but it is very challenging to get high accuracy while containing low computational complexity …
but it is very challenging to get high accuracy while containing low computational complexity …
Visual cascade analytics of large-scale spatiotemporal data
Many spatiotemporal events can be viewed as contagions. These events implicitly
propagate across space and time by following cascading patterns, expanding their …
propagate across space and time by following cascading patterns, expanding their …
Identifying critical and vulnerable links: A new approach using the Fisher information matrix
In traffic networks, some elements are more prone to suffer or to create disruptive situations,
and the identification of these elements becomes a challenge due to the large number of …
and the identification of these elements becomes a challenge due to the large number of …