Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

systems combined with image processing compared to all other models. Despite the facts that were included on these parametric, non-parametric and hybrid prediction models, in the literaturesection, there can be seen a vast number of projects and research approaches subject to predictions and forecasting models which evaluate the existing gaps. Further, when the literature on traffic prediction methods are referred to, there can be seen an outstanding tendency toward using LSTM neural networks for traffic predictions, especially in recently published research articles. Similarly, novel projects and research have given great attention to these LSTM neural networks. The reference [33] evaluates the recent rise in the usage of LSTM algorithms for traffic prediction. It reveals that now the LSTM model has become common and prominent. According to another research carried out on LSTM[34], most prediction approaches have concentrated on accuracy rather than immediacy. LSTM neural network is an improved version of RNN where the vanishing gradient problem and long- term dependency of recurrent neural networks are successfully overcome. Input, output, and forget gates are three gates that are introduced in LSTM. However, the reference [35] claims that even though most of the novel traffic management approaches used Long short- term memory (LSTM) models, those existing projects and their models have failed to address the issue of massive traffic flow data being processed simultaneously with parallel to computing and distributed data storage. But it also emphasizes that the LSTM model is a better prediction model for more random and time-varying predictions such as traffic flow [35]. According to reference [40], traffic prediction research should focus on the data-intensive era, which is now missing. The existing traffic categorization algorithms are ineffective in low-light situations [41]. Also, there can be seen a lack ofstudies focusing on the time series for the Internet of Things (IoT) traffic forecast [33]. Existing research has not properly simulated or created to be compatible with the dynamic trafficpatterns of irregular locations [33]. Traffic prediction studies are controversial due to a lack of more efficient computational methodologies and algorithms, as well as excellent quality data. Research article [43] indicated that the performance of the Convolutional Neural Network (CNN) for traffic prediction was somewhat disappointing based on the implementations of prior research.Table II compares the strengths and weaknesses of eachprediction model. Table II: Prediction Models Comparison PredictionModel Prediction Models Comparison Strengths Weaknesses Mean Averagemodel • Low Prediction Error. • Poor functionality in the presence of noise. • Average of all inputs are needed for the predictions. High dependency on recorded data. Linear Regression Method • Low Prediction Error. • Predicts the next variable onlineusing real data. • Poor functionality amid the noise and other disturbances. MaximumLikelihood (ML) • Low Prediction Error. • Robust for sensor failures and rapidly changing conditions. • High dependency on recorded data. Exponential Smoothing Method • Low Prediction Error. • Poor functionality amid the noise and other disturbances. • Difficulties to determine constant coverage. ARIMA Model • Low Prediction Error.Simplicity. • More mathematical model • Obtain the relationship between past and future data. • Poor functionality amid the noise and other disturbances. • High dependency on recorded data. MLP Model • High Accuracy. • High dependency on recorded data. © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 43 ( )D Year 2023 Traffic Flow Forecast based on Vehicle Count