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

b) Traffic Behavior Analysis After observing the traffic behavior of a day, the traffic behavior of a week was also plotted. There also the traffic behavior of each day takes normal distribution behavior. Therefore, the traffic behavior of a week was a combination of seven normal distributions. Additionally, after gathering traffic data over a month, the traffic behavior of each week was also plotted to identify whether the traffic flow remains the same following a pattern every week or is there any significant increase or decrease in the traffic behavior. Belowfigure 6 represents the overall traffic behavior of the entire month. These plots which were plotted observing the traffic behavior of a day, a week and entire month were helpful in understanding the traffic pattern as well as in deciding the window size that should be taken to train the LSTM neural network since there was not any other source to be observed to identify the traffic patterns. c) Prediction Results When obtained data patterns were observed, it was recognized that there could be significant deviations in traffic flow after intervals of 60 minutes which means during an hour, there could be seen slight differences but from hour to hour there can be seen significant increasements or decrements in traffic behavior. Therefore, first it was identified it is better totake 12 data points at a time to train the prediction model. Then the desired window size was kept to twelve and using data gathered over a month the very first LSTM neural network model was trained. Below figure 7 illustrates the resulted predicted traffic behavior vs test data plot and figure8 illustrates the entire plot of the trained data, test data and resulted predicted traffic data. Finding the best traffic prediction model is one major research objective of this project. Therefore, more than ten LSTM neural network models were created and evaluated in terms of accuracy to identify the most suitable forecasting model. Here all the model sets that were tested able to result accuracy more than 70%. And no model exhibits large deviations in predicted traffic behavior vs test data plot proving that fact. Further, it seems that the strategy that has been carried out by changing the window size and the volume of the input data as the variable parameters to figure out the best model was successful because there can be seen a noticeable pattern in the prediction accuracies. Overall Traffic Behavior of Each Week Figure 7: Predicted Traffic Behavior vs Test Data Plot © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 47 ( )D Year 2023 Traffic Flow Forecast based on Vehicle Count Figure 6: