Machine learning for security in vehicular networks: A comprehensive survey
Machine Learning (ML) has emerged as an attractive and viable technique to provide
effective solutions for a wide range of application domains. An important application domain …
effective solutions for a wide range of application domains. An important application domain …
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 …
Transfer learning with graph neural networks for short-term highway traffic forecasting
Large-scale highway traffic forecasting approaches are critical for intelligent transportation
systems. Recently, deep-learning-based traffic forecasting methods have emerged as …
systems. Recently, deep-learning-based traffic forecasting methods have emerged as …
Toward deep transfer learning in industrial internet of things
Machine learning techniques have been widely adopted to assist in data analysis in a
variety of Internet of Things (IoT) systems. To enable flexible use of trained learning models …
variety of Internet of Things (IoT) systems. To enable flexible use of trained learning models …
Privacy-preserving distributed transfer learning and its application in intelligent transportation
With the rapid development of intelligent transportation systems (ITS), more and more
intelligent applications for ITS have received widespread attention, such as the vehicle …
intelligent applications for ITS have received widespread attention, such as the vehicle …
Exploring demand patterns of a ride-sourcing service using spatial and temporal clustering
TLK Liu, P Krishnakumari, O Cats - 2019 6th international …, 2019 - ieeexplore.ieee.org
On-demand transport has become a common mode of transport with ride-sourcing
companies like Uber, Lyft and Didi transforming the mobility market. Recurrent patterns in …
companies like Uber, Lyft and Didi transforming the mobility market. Recurrent patterns in …
Deep learning and low-discrepancy sampling for surrogate modeling with an application to urban traffic simulation
C Cervellera, D Macciò… - 2021 International Joint …, 2021 - ieeexplore.ieee.org
In this paper we investigate deep learning architectures combined with low-discrepancy
sampling as surrogate models. The aim is to provide a quick estimate of the outcome of an …
sampling as surrogate models. The aim is to provide a quick estimate of the outcome of an …
Tleta: Deep transfer learning and integrated cellular knowledge for estimated time of arrival prediction
Estimated time of arrival (ETA) is a critical component of intelligent transportation systems.
Though many tools exist for ETA, ETA for special vehicles, such as ambulances, fire …
Though many tools exist for ETA, ETA for special vehicles, such as ambulances, fire …
Modelling and reasoning techniques for context aware computing in intelligent transportation system
The emergence of Internet of Things technology and recent advancement in sensor
networks enabled transportation systems to a new dimension called Intelligent …
networks enabled transportation systems to a new dimension called Intelligent …
Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?
Network-level traffic condition forecasting has been intensively studied for decades.
Although prediction accuracy has been continuously improved with emerging deep learning …
Although prediction accuracy has been continuously improved with emerging deep learning …