Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …
Urban big data fusion based on deep learning: An overview
Urban big data fusion creates huge values for urban computing in solving urban problems.
In recent years, various models and algorithms based on deep learning have been …
In recent years, various models and algorithms based on deep learning have been …
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …
predict the future number of incoming and outgoing people in regions. Measuring the …
Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction
Traffic prediction has drawn increasing attention in AI research field due to the increasing
availability of large-scale traffic data and its importance in the real world. For example, an …
availability of large-scale traffic data and its importance in the real world. For example, an …
Deep learning on traffic prediction: Methods, analysis, and future directions
X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
Deep multi-view spatial-temporal network for taxi demand prediction
Taxi demand prediction is an important building block to enabling intelligent transportation
systems in a smart city. An accurate prediction model can help the city pre-allocate …
systems in a smart city. An accurate prediction model can help the city pre-allocate …
A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing
Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
Contextualized spatial–temporal network for taxi origin-destination demand prediction
Taxi demand prediction has recently attracted increasing research interest due to its huge
potential application in large-scale intelligent transportation systems. However, most of the …
potential application in large-scale intelligent transportation systems. However, most of the …
The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms
Taxi-calling apps are gaining increasing popularity for their efficiency in dispatching idle
taxis to passengers in need. To precisely balance the supply and the demand of taxis, online …
taxis to passengers in need. To precisely balance the supply and the demand of taxis, online …
Predicting taxi–passenger demand using streaming data
Informed driving is increasingly becoming a key feature for increasing the sustainability of
taxi companies. The sensors that are installed in each vehicle are providing new …
taxi companies. The sensors that are installed in each vehicle are providing new …