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

Figure 9: Predicted Traffic Behavior vs Test Data Plots Figure 10: Accuracy Variations of the Prediction Model Sets Further, the accuracy results of these test models which are presented above shown in Table III, IV, and Table V emphasize that both window size and the volume of the input data creates a significant impact on the prediction accuracy of the prediction models. The accuracy test results of all the 3 model sets show that all of them were above 70%. Above Table III includes accuracy results of the training model set 1 which was created by setting the window size to12 while changing the input data volume from 2016 data points to 8064 data points. As per described in the methodology section, when 2016 input data set is considered, it includes the data gathered during the last week of the monthand that data was fed to the system for training. Accordingly, the other models were tested using increasing input data points while keeping the window size constant. In model set 1, there can be seen a gradual increase in accuracy when the input datavolume gets larger. In the second model set (represented in Table IV), the input data volume was set to 8064 data points and the windowsize was changed to 6, 12, 24, 60 and 288 (explained in the methodology section). Even though the accuracy has increased in the first three models, there can be seen a noticeable decrease in accuracy with larger window sizes. This implies that the most suitable window size is 24. Third model set in which results are presented in Table V, the window size was set to 24 and once again the input data volume was changed from 2016 data points to 8064 data points. There also can be seen a gradual increase in accuracywhen the input data volume is getting larger. Above figure 9 illustrates several predicted traffic behavior vs test data plots. All of them are quite similar and both predicted vs actual traffic behavior lies on top of each other showing a closer behavior. Similarly, there are no larger deviations. Figure 10 includes the graphical representation ofthe above Tables III, IV and V. It also provides graphical comparison on the accuracy results that were obtained from each model set. As illustrated, there can be seen noticeable changes whenvarying both window size and the volume of input data. A gradual increase in accuracy can be seen when increasing theinput data volume. Meanwhile, it implies that the window sizeshould remain under a fixed value, and increasing it enhances the accuracy but after a certain value, the accuracy may decrease if it is further increased. Therefore, according to the obtained results it was identified that the highest prediction accuracy could be obtained by setting the window size to 24 windows in whichthe data gathered for two hours will be fed into the system to be trained at a time and increasing the input data volume to the greatest extent possible. Similarly, resulted accuracy values also indicate that the model should be trained using 80% dataand tested using 20% data from the gathered data in order to obtain higher precision. VI. C onclusion The main objective of this project was to introduce A better traffic management system and traffic forecasting system to reduce traffic congestion, © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 49 ( )D Year 2023 Traffic Flow Forecast based on Vehicle Count