Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Deep learning in mobile and wireless networking: A survey
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …
services pose unprecedented demands on mobile and wireless networking infrastructure …
[HTML][HTML] Deep learning for network traffic monitoring and analysis (NTMA): A survey
Modern communication systems and networks, eg, Internet of Things (IoT) and cellular
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
RL/DRL meets vehicular task offloading using edge and vehicular cloudlet: A survey
The last two decades have seen a clear trend toward crafting intelligent vehicles based on
the significant advances in communication and computing paradigms, which provide a safer …
the significant advances in communication and computing paradigms, which provide a safer …
Deep learning for edge computing applications: A state-of-the-art survey
With the booming development of Internet-of-Things (IoT) and communication technologies
such as 5G, our future world is envisioned as an interconnected entity where billions of …
such as 5G, our future world is envisioned as an interconnected entity where billions of …
Machine learning for geographically differentiated climate change mitigation in urban areas
Artificial intelligence and machine learning are transforming scientific disciplines, but their
full potential for climate change mitigation remains elusive. Here, we conduct a systematic …
full potential for climate change mitigation remains elusive. Here, we conduct a systematic …
Foresee urban sparse traffic accidents: A spatiotemporal multi-granularity perspective
Traffic accident has become a significant health and development threat with rapid
urbanizations. An accurate urban accident forecasting enables higher-quality police force …
urbanizations. An accurate urban accident forecasting enables higher-quality police force …
[HTML][HTML] A sustainable smart mobility? Opportunities and challenges from a big data use perspective
This paper discusses the recent insights on the Big Data role in the sustainability of smart
mobility. Systematic Literature Review is applied to scientific publications web repositories …
mobility. Systematic Literature Review is applied to scientific publications web repositories …
Context-aware taxi dispatching at city-scale using deep reinforcement learning
Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among
different locations in a city. Recent advances primarily rely on deep reinforcement learning …
different locations in a city. Recent advances primarily rely on deep reinforcement learning …
TrafficBERT: Pre-trained model with large-scale data for long-range traffic flow forecasting
Traffic flow prediction has various applications such as in traffic systems and autonomous
driving. Road conditions have become increasingly complex, and this, in turn, has increased …
driving. Road conditions have become increasingly complex, and this, in turn, has increased …