A review of irregular time series data handling with gated recurrent neural networks
Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor
systems as well as the continued use of unstructured manual data recording mechanisms …
systems as well as the continued use of unstructured manual data recording mechanisms …
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 …
A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
In this modern era, traffic congestion has become a major source of severe negative
economic and environmental impact for urban areas worldwide. One of the most efficient …
economic and environmental impact for urban areas worldwide. One of the most efficient …
Traffic flow forecasting with spatial-temporal graph diffusion network
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of
spatial-temporal mining applications, such as intelligent traffic control and public risk …
spatial-temporal mining applications, such as intelligent traffic control and public risk …
[HTML][HTML] DeepTSP: Deep traffic state prediction model based on large-scale empirical data
Real-time traffic state (eg, speed) prediction is an essential component for traffic control and
management in an urban road network. How to build an effective large-scale traffic state …
management in an urban road network. How to build an effective large-scale traffic state …
Autost: Efficient neural architecture search for spatio-temporal prediction
Spatio-temporal (ST) prediction (eg crowd flow prediction) is of great importance in a wide
range of smart city applications from urban planning, intelligent transportation and public …
range of smart city applications from urban planning, intelligent transportation and public …
LSTM-based indoor air temperature prediction framework for HVAC systems in smart buildings
Accurate indoor air temperature (IAT) predictions for heating, ventilation, and air
conditioning (HVAC) systems are challenging, especially for multi-zone building and for …
conditioning (HVAC) systems are challenging, especially for multi-zone building and for …
Spatial-temporal convolutional graph attention networks for citywide traffic flow forecasting
Traffic flow prediction plays an important role in many spatial-temporal data applications, eg,
traffic management and urban planning. Various deep learning techniques are developed to …
traffic management and urban planning. Various deep learning techniques are developed to …
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 …