A survey on deep learning and its applications
Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …
Ridesourcing systems: A framework and review
With the rapid development and popularization of mobile and wireless communication
technologies, ridesourcing companies have been able to leverage internet-based platforms …
technologies, ridesourcing companies have been able to leverage internet-based platforms …
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 …
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 …
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 …
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 …
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 …
Deep learning for intelligent transportation systems: A survey of emerging trends
M Veres, M Moussa - IEEE Transactions on Intelligent …, 2019 - ieeexplore.ieee.org
Transportation systems operate in a domain that is anything but simple. Many exhibit both
spatial and temporal characteristics, at varying scales, under varying conditions brought on …
spatial and temporal characteristics, at varying scales, under varying conditions brought on …
A survey on deep learning for human mobility
The study of human mobility is crucial due to its impact on several aspects of our society,
such as disease spreading, urban planning, well-being, pollution, and more. The …
such as disease spreading, urban planning, well-being, pollution, and more. The …
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 …