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 …
Fusion of engineering insights and emerging trends: Intelligent urban traffic management system
Traffic congestion is a great concern, especially in urban areas where the vehicles' number
on roads continues to intensify significantly against the slow development of road …
on roads continues to intensify significantly against the slow development of road …
Bio-inspired computation: Where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …
with the mimicking of behavioral patterns and social phenomena observed in nature towards …
Multi-range attentive bicomponent graph convolutional network for traffic forecasting
Traffic forecasting is of great importance to transportation management and public safety,
and very challenging due to the complicated spatial-temporal dependency and essential …
and very challenging due to the complicated spatial-temporal dependency and essential …
Gated residual recurrent graph neural networks for traffic prediction
Traffic prediction is of great importance to traffic management and public safety, and very
challenging as it is affected by many complex factors, such as spatial dependency of …
challenging as it is affected by many complex factors, such as spatial dependency of …
Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks
Traffic flow prediction is crucial for public safety and traffic management, and remains a big
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …
Real-time deep reinforcement learning based vehicle navigation
Traffic congestion has become one of the most serious contemporary city issues as it leads
to unnecessary high energy consumption, air pollution and extra traveling time. During the …
to unnecessary high energy consumption, air pollution and extra traveling time. During the …
Vppcs: Vanet-based privacy-preserving communication scheme
MA Al-Shareeda, M Anbar, S Manickam… - IEEE Access, 2020 - ieeexplore.ieee.org
Over the past years, vehicular ad hoc networks (VANETs) have been commonly used in
intelligent traffic systems. VANET's design encompasses critical features that include …
intelligent traffic systems. VANET's design encompasses critical features that include …
Dynamic traffic correlations based spatio-temporal graph convolutional network for urban traffic prediction
Accurate urban traffic prediction is a critical issue in Intelligent Transportation Systems (ITS).
It is challenging since urban traffic usually indicates high dynamic spatio-temporal …
It is challenging since urban traffic usually indicates high dynamic spatio-temporal …
Traffic flow prediction over muti-sensor data correlation with graph convolution network
Accurate and real-time traffic flow prediction plays an important role in improving the traffic
planning capability of intelligent traffic systems. However, traffic flow prediction is a very …
planning capability of intelligent traffic systems. However, traffic flow prediction is a very …