## 2 Related Works  
[PERSON] et al. [11] suggest a short-term traffic flow forecasting technique on the basis of a CNN-DL structure. In the presented structure, the optimum input duration lags and spatial data volumes were fixed by a spatio-temporal feature selection algorithm (STFSA), and the chosen spatio-temporal traffic flow features were derived from actual data and transformed into a 2-dimensional matrix. The CNN later studied such features for framing a predictive method. In [12], a hybrid technique compiling FNN and fuzzy rough set (FRS) can be suggested for imputation of missed traffic data. At first, FNN can be utilized for data classification; next, the KNN technique can be employed for determining optimal number of data leveraged to predict missing data in every category; lastly, the FRS was leveraged for imputing missed values. In [13], deep feature learning techniques were recommended for predicting short-term traffic flow in the succeeding multiple steps, utilizing supervised learning approaches. In order to reach traffic flow forecasting for the following day, an advanced multiobjective PSO method is implemented for optimizing certain variables in DBN. The modified method could foster the accuracy of the prediction fallouts and bolster their multiple step prediction capability. [PERSON] et al. [14] suggest a single-stage DNN-YOLOv3 (you only look once)-DL, that depends on the Tensorflow structure for improvising this issue. The network framework can be maximized by presenting the ideology of spatial pyramid pooling, afterward, the loss function was redefined, and a weight regularization technique was presented, in which, the statistics and real-time detections of traffic flow are applied efficiently. The optimization method uses the DL-CAR dataset for experimentations and end-to-end network training with datasets in various cases and weathers. In [15], a traffic flow detection method depending on DL on the edge node is suggested. Initially, the authors suggest a vehicle detection approach on the basis of the YOLOv3 method trained with a high volume of traffic data. They subsequently then pruned the method for ensuring competence on the edge equipment. In [16], a novel end-to-end hybrid DL network method, termed M-B-LSTM, was suggested for short-term traffic flow forecasting in this work. In the M-B-LSTM method, online self-learning networks can be built as a data map layer for learning and equalizing the traffic flow statistic dispersal to reduce the impact of overfitting issues and distribution imbalance at the time of network learning. [PERSON] et al. [17] suggest a new short-run traffic flow forecasting technique depends on an adaptive multikernel SVM (AMSVM) with spatial-temporal co-relation, termed AMSVM-STC. Initially, explore the randomness as well as nonlinearity of traffic flow, and hybrid polynomial kernel and Gaussian kernel for constituting the AMSVM. Secondly, the variables of AMSVM are optimized with the adaptive PSO method and recommends a new technique to constitute the hybrid kernel's weight adjust adaptively in accordance with changing tendency of realistic traffic flow. [PERSON] et al. [18] presents a short-term traffic flow predictive approach which integrates community detection-based federated learning with graph convolutional network (GCN) for alleviating the time consuming trained, superior communication costs, and data privacy risks of global GCNs as the count of data improves. The federated community GCN (FCGCN) is attain accurate, timely, and safe traffic state predictive from the period of big traffic information that is an important for the effective function of intelligent transportation methods. [PERSON] et al. [19] examines a process for screening spatial time-delayed traffic sequence dependent upon the maximal data coefficient. The selective time-delayed traffic sequence is changed as to traffic state vector in that traffic flow was predictive by implementing the integration of SVM and KNN techniques.