Abstract
Cooperative Intelligent Transport System (C-ITS) plays a vital role in the future road traffic management system. A vital element of C-ITS comprises vehicles, road side units, and traffic command centers, which produce a massive quantity of data comprising both mobility and service-related data. For the extraction of meaningful and related details out of the generated data, data science acts as an essential part of the upcoming C-ITS applications. At the same time, prediction of short-term traffic flow is highly essential to manage the traffic accurately. Due to the rapid increase in the amount of traffic data, deep learning (DL) models are widely employed, which uses a non-parametric approach for dealing with traffic flow forecasting. This paper focuses on the design of intelligent deep learning based short-term traffic flow prediction (IDL-STFLP) model for C-ITS that assists the people in various ways, namely optimization of signal timing by traffic signal controllers, travelers being able to adapt and alter their routes, and so on. The presented IDL-STFLP model operates on two main stages namely vehicle counting and traffic flow prediction. The IDL-STFLP model employs the Fully Convolutional Redundant Counting (FCRC) based vehicle count process. In addition, deep belief network (DBN) model is applied for the prediction of short-term traffic flow. To further improve the performance of the DBN in traffic flow prediction, it will be optimized by Quantum-behaved bat algorithm (QBA) which optimizes the tunable parameters of DBN. Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flow in real-time with a maximum performance under dissimilar environmental situations.
Highlights
Cooperative Intelligent Transport System (C-ITS) is a well-known and effective model which aspires to enhance road safety, traffic control, and driver security
A wide range of experimentation analyses was performed and the experimental results denoted that the presented IDL-STFLP method can count vehicles and predict traffic flow in real-time with maximum performance under dissimilar environmental situations
The presented IDL-STFLP model operates on two main stages namely vehicle counting and traffic flow prediction
Summary
Cooperative Intelligent Transport System (C-ITS) is a well-known and effective model which aspires to enhance road safety, traffic control, and driver security. C-ITS is used to resolve with maximum data interchanging among diverse C-ITS utilities like vehicles, RSUs as well as TCCs. data analytics is a major device which can be applied for extracting applicable outcomes. The massive C-ITS domains gather and examine the sensors details and offer services to individuals In this method, 2 operators have been adopted namely, smart parking model as well as road condition tracking. The effect of solving complex scenarios to gain précised target prediction, Machine Learning (ML) models and classifiers are employed extensively prior to applying Deep Learning (DL) which is considered as the major stream of computer vision. The traditional schemes for vehicle counting depend upon the video classification as DL-based tracking approaches, online methods (Markov decision process (MDP)), and batch-relied models (Internet of Underwater Things (IOUT)). To resolve the tracking complexities formed by different movable scenes, defined occlusion, deformation, and vehicle scale extensions, structure of efficient vehicle monitoring technology plays a vital role in performance estimation
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