Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as
subway/bus stations, is a practical application and of great significance for urban traffic …
subway/bus stations, is a practical application and of great significance for urban traffic …
Traffic accident detection and condition analysis based on social networking data
Accurate detection of traffic accidents as well as condition analysis are essential to
effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be …
effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be …
Smartphones as an integrated platform for monitoring driver behaviour: The role of sensor fusion and connectivity
S Kanarachos, SRG Christopoulos… - … research part C: emerging …, 2018 - Elsevier
Nowadays, more than half of the world's web traffic comes from mobile phones, and by 2020
approximately 70 percent of the world's population will be using smartphones. The …
approximately 70 percent of the world's population will be using smartphones. The …
Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction
Urban traffic passenger flows prediction is practically important to facilitate many real
applications including transportation management and public safety. Recently, deep …
applications including transportation management and public safety. Recently, deep …
A survey of methods and technologies for congestion estimation based on multisource data fusion
Traffic congestion occurs when traffic demand is greater than the available network capacity.
It is characterized by lower vehicle speeds, increased travel times, arrival unreliability, and …
It is characterized by lower vehicle speeds, increased travel times, arrival unreliability, and …
Improving stock market prediction via heterogeneous information fusion
Traditional stock market prediction approaches commonly utilize the historical price-related
data of the stocks to forecast their future trends. As the Web information grows, recently …
data of the stocks to forecast their future trends. As the Web information grows, recently …
Modeling real-time human mobility based on mobile phone and transportation data fusion
Even though a variety of human mobility models have been recently developed, models that
can capture real-time human mobility of urban populations in a sustainable and economical …
can capture real-time human mobility of urban populations in a sustainable and economical …
Multi-task adversarial spatial-temporal networks for crowd flow prediction
Crowd flow prediction, which aims to predict the in-out flows (eg the traffic of crowds, taxis
and bikes) of different areas of a city, is critically important to many real applications …
and bikes) of different areas of a city, is critically important to many real applications …
Wsip: Wave superposition inspired pooling for dynamic interactions-aware trajectory prediction
Predicting motions of surrounding vehicles is critically important to help autonomous driving
systems plan a safe path and avoid collisions. Although recent social pooling based LSTM …
systems plan a safe path and avoid collisions. Although recent social pooling based LSTM …
Context-aware road travel time estimation by coupled tensor decomposition based on trajectory data
Urban road travel time estimation and prediction on a citywide scale is a necessary and
important task for recommending optimal travel paths. However, this problem has not yet …
important task for recommending optimal travel paths. However, this problem has not yet …