In the last few years, there has been an exponential increase in the usage of the
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …

Traffic flow prediction is important for urban planning and traffic congestion alleviation as
well as for intelligent traffic management systems. Due to the periodic characteristics and …

Accurate and real-time prediction of network traffic can not only help system operators
allocate resources rationally according to their actual business needs but also help them …

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation
Systems. Accurate prediction result is the precondition of traffic guidance, management, and …

Congestion of traffic is a key problem faced in a majority of metro cities, especially in the
developing world. Traffic congestion comprises of queues, reduced speeds, and increased …

Accurate and real-time traffic flow prediction plays a central role for efficient traffic
management. Software Defined Networking (SDN) is one of the key concerns in networking …

This paper presents the development and evaluation of short‐term traffic prediction models
using unidirectional and bidirectional deep learning long short‐term memory (LSTM) neural …

The contradiction between industrial agglomeration and energy constrains the development
of cities. Government competition can affect the flow of factors for industrial agglomeration …

In this paper, Deep Neural Networks (DNN) with Bat Algorithms (BA) offer a dynamic form of
traffic control in Vehicular Adhoc Networks (VANETs). The former is used to route vehicles …

Long short-term memory (LSTM) models provide high predictive performance through their
ability to recognize longer sequences of time series data. More recently, bidirectional deep …