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

b) Prediction Methods Traffic flow is a real-time, totally non-linear, high- dimensional, and non-stationary random process. Reviewing the literature emphasizes that vehicle forecasting is a common research topic and that more research has been done while many approaches have been taken. Most of the research articles have a special focus on these traffic predictions due to the uncertainty and random non-linearity of the traffic flow. Similarly, most of them have introduced Traffic predicting and forecasting traffic strategies which assist to minimize these increasing traffic jams [27], [28]. According to the reference [28], traffic forecasting is classified into two basic categories: long-term prediction and short-term prediction. The projections for the near future are known as short-term predictions typically these short-term predictions are for the next 5 to 10 minutes in immediate future. Short-term predictions are a common prediction method since to the changing weather conditions, cultural or political occasions, and events, road accidents may cause sudden changes in traffic conditions [28]. There can be seen several research articles which evaluatethe accuracy and the efficiency of several prediction models comparing one another. Among them [27], [28] reference emphasizes that both data-driven and experimental traffic flow prediction approaches can be classified as parametric, non-parametric, or hybrid, each having its own set of benefitsand drawbacks [28]. The majority of traffic flow prediction research has been conducted under regular traffic conditions,while unusual traffic variables such as climate, the presence of noise in the data, and highway disruptions have seldom been addressed. Providing a complete detailed analysis of these three prediction methods, above mentioned reference emphasizes linear regression, maximum likelihood (ML), Historical Mean Average, exponential smoothing method, and time series model as parametric prediction approaches [28]. Moreover, it claimed that the parametric prediction method ismore accurate than the other two methods, still its poor functionality amid the noise and other disturbances is an encountered major drawback [28]. Extensive research by Van Lint and Hoogendoorn which provides an overview of predictive models for short-term traffic forecasting also emphasizes that forecasting methods can be divided into three major groups: parametric, non- parametric, and hybrid (naive) [29]. i. Non-Parametric Models Non-parametric models imply that the number of parameters which assigned to a model is flexible. This means it is not fixed, in which the model structure and parameters should be developed based on the available data. Usually, theamount of data should be significant in contrast to the other two approaches. These models have the benefit of allowing for the discovery of intricate non-linear correlations between traffic factors. However, the disadvantage of these models is that while the model's structure is derived from the data, unanticipated events and outliers may have an impact [29]. The intricacy of these models, as well as their reliance on vast amounts of data, are also other significant drawbacks. Non- parametric prediction approaches use current models rather than traditional models to estimate traffic flow in proportion to road conditions. Neural networks, such as the multilayer perceptron (MLP), time-delay neural network (TDNN), and radial basis function (RBF) are the most popular and prominent non-parametric approaches. Besides these neural networks, Fuzzy [30], Bayesian networks k-nearest neighbor (KNN)[31], support vector machine, and wavelet are other non- parametric methods used for predictions [28]. Neural networks are the most common and popular model that is used for traffic predictions. They consist of the ability to model and simulate complicated non-linear relationships [29]. There can be seen different neural network types such as Feed forward artificial neural networks, Convolutional Neural Network (CNN), Recurrent neural networks (RNN) as well as Long Short- Term Memory networks (LSTM) based on their training procedure, internal structure, methods of preprocessing input data and their models including spatial ortemporal patterns. Among them, the feed forward artificial neural network is the simplest neural network category whilethe LSTM is the most powerful model to process sequential data. The type of the neural network varies depending upon the task being performed with it. Typically, a neural network comprises an input layer, several hidden layers and an output layer [29]. According to the research paper by Van Hinsbergen, Van Lint, et al., a typical neural network might deliver reliable findings in terms of extensions required for higher accuracy in traffic predictions [29]. ii. Parametric Models Parametric models imply that the model's structure and the number of parameters are tightly established, and the model'sparameters must be derived using data. The advantage of suchmodels is that unseen cases and incidents can be captured. Another advantage is the necessity of less data. Some models are capable of offering higher accuracy even with less computational work [29]. The hybrid model is the combined version of both parametric and non-parametric prediction models where the accuracy of the prediction is higher than the other two types. Most hybrid prediction models are combinations of neural networks and other parametric and non-parametric models such as ARIMA models, MLP, and fuzzy. According to the reference [28] the Neural network-based MLP model is the most suitable prediction model for data-driven traffic forecasting © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 42 ( ) Year 2023 D Traffic Flow Forecast based on Vehicle Count