… proposed for NTMA applications. Finally, we discuss key challenges, open issues, and …
generate different types of data resulting in heterogeneity in both network traffic and network …

… , these models were proposed at a time where traffic data were … challenges that the field of
deep neural network for traffic … used by drivers and traffic management bureau alike to make …

… Therefore, deep learning has a good advantage in solving … algorithm based on deep learning
proposed in this research … algorithm based on deep learning proposed in this research. …

… in SC, no survey has been dedicated mainly on deep learning for … propose to present recent
development, challenges and future research direction on the applications of deep learning …

… pressure to urban traffic management. Intelligent Transportation … [83] proposed a deep
learning framework that introduced … results in different traffic prediction tasks, comprehensively …

… can provide efficient traffic management, accurate traffic resources … traffic data aroundthe-clock,
which are heterogeneous data, … For each challenge, we conclude multiple deep learning-…

… proposed a deep neural network (DNN) based model which can detect the drowsiness of the
driver. The proposed … Massive heterogeneous data is generated from multiple sources such …

… However, there are still many critical issues of employing machine learning in networks, …
has been proposed for heterogeneous networks recently. Besides the deep learning based …

… It also reveals the role of advancing deep learning methods in … own issues and challenges
in solving TSC and the network-… , fleet management, toll plaza, traffic control architecture …

… such spatial heterogeneity in the cases where only road network … To address these challenges,
we propose a novel traffic … we propose an effective deep learning based neural network …