## 1 Introduction  
As a new form of intellectually complex mechanism, with high interaction and integration among multidimensional heterogeneous physical substances in network atmospheres [1, 2], the cyber-physical system (CPS) compiles control, computing, and communication technologies to offer a practicable solution to and advanced technologies for the new generation of intelligent transportation system (ITS). This therefore is the key advancement direction of the CPS and resolves the issues of intellectual real-time target control and optimal dispatch of ITS [3]. Meanwhile, the issue of dense functions of large-scale data-computing and optimum control-scheduling methods in large-scale ITS is resolved by the speedy advancement of cloud computing (CC) technology. The basic principle of this was dispensing computing tasks on a great number of cloud-distributed computers; ITS management departments could then match CC sources to ITS cloud-controlled applications, evaluating storage systems and computers as required [4]. The implementation of CPS and CC technology makes it possible to attain, transfer and compute traffic data practically, and the implementation of the dynamic matrix method and artificial intelligence (AI) method could forecast traffic data in the next moment in advance [5].  
Real-time precise short-time traffic flow forecasting could provide traffic guidance for traffic participants by selecting a suitable travel route, and aid the traffic controllers to have a fair control strategy for relieving traffic congestion [6]. Traffic flow becomes a time sequence data, having robust cyclicity and regularity that were considered as the base for precise estimation. However, its uncertainty and randomness raise prediction difficulties. Therefore, short-time traffic flow forecasting becomes necessary and challenging one in research domains and transport management [7]. Over the last few years, more techniques were deployed for forecasting short-term traffic flow, such as the autoregressive integrated moving average (ARIMA), fuzzy theory, artificial neural network (ANN), and Kalman filter. Such techniques proved to be helpful in deriving traffic flow temporary tendency and forecasting the future traffic flow. It was discovered from the literature that AI-related methods have been extensively utilized for object analytics and detection in ITS. However, such AI-enabled methods need precise perception [8]. However, prevailing approaches produce several mistakes at the time of execution, which may not applicable for realistic data analytics in ITS.  
During the past few decades, several research proposals were suggested for enhancing self-learning approaches to dynamic and complex applications of the transport system [9]. Self-learning methods for traffic prediction could be widely split into 2 parts: nonparametric and parametric. In this context, a nonparametric method termed deep learning (DL) was found to be helpful for traffic flow forecasting with multidimensional features [10]. DL was a subset of machine learning (ML) that depends on the idea of deep neural network (DNN) and it was broadly utilized for object recognition, data classification, and natural language processing (NLP).  
This manuscript introduces an autonomous short-term traffic flow prediction using an optimal hybrid deep belief network (AST2 FP-OHDBN) model. The presented AST2 FP-OHDBN model initially normalizes the traffic data using min-max normalization. In addition, the HDBN model is employed for forecasting the traffic flow in the near future, by the use of DBN with an adaptive learning step approach to enhance the convergence rate. To enhance the predictive accuracy of the DBN model, the pelican optimization algorithm (POA) is exploited as a hyperparameter optimizer, which in turn enhances the overall efficiency of the traffic flow prediction process. For assuring enhanced predictive outcomes of the AST2 FP-OHDBN algorithm, a wide-ranging experimental analysis can be carried out.