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

during the last week of the month, which is fed to the system for training. Accordingly, the 4032 data points indicates the data gathered over the last two weeks and 6048 indicates the collection of data over three weeks and 8064 data points represents the data gather over a month. (i.e., a separate data set with 2016 data points collected over another week was employed as test data to test the models.) In the second model set, depending upon the results of thefirst four models, input data volume was set to 8064 data points and the window size was changed to 6, 12, 24, 60 and 288. Window size 6 means 6 data points are fed into the system at a time, which means 30- minute (1/2 hour) intervals. 12 means 1-hour data is fed at a time. Likewise, three other models were trained for 2 hours, 5 hours and 24 hours. Depending upon the results of those five models, the desiredwindow size was determined. And then in the third model set, the window size was set to 24 and once again the input data volume was changed from 2016 data points to 8064 data points. After obtaining all of these prediction model sets, all the models that have been trained varying both window size and the volume of input data delivered to train the models were critically evaluated in terms of accuracy in order to identify the most suitable prediction method. Accuracy formulas provide a vital contribution to projects since precisecalculations are always helpful to critically evaluate the resulted outcomes to determine the most suitable solutions to apply. Accordingly, all accuracy-related calculations of this project (sensor accuracy calculations, prediction models’ accuracy calculations) have been done using the following two equations which are used to measure the world- acceptedaccuracy in accordance with IEEE standards [51]. The accuracy formula provides accuracy as a difference of error rate from 100%. To find accuracy, first it is needed to calculate the error rate. And the error rate is the percentage value of the difference between the observed and the actual value, divided by the actual value [51]. Therefore, below equation 1 was utilized to determine the error rate. Once the error rate is calculated the accuracy is determined using the below equation 2 as the difference of error rate from100%. Accuracy = 100% − Error Rate V. R esults and V alidation a) Results of the Sensor Accuracy Test First of all, the accuracy of the sensor was tested considering the data gathered during a random day. Actual vehicular counts were taken by counting the actual vehicle count and the sensor detected vehicle count was directly obtained from the sensor records. Then the actual vehicle count vs sensor-detected vehicle count was plotted and the accuracy of the sensor has been tested considering 200 data points. Obtained the accuracy results of the sensor was 89.86%. Figure 5 illustrates the behavioral differences between actual vehicle count and sensor- detected vehicle count. This emphasizes that sensor detected vehicle count is almost closer actual vehicle count. Similarly, this sensor accuracy plot is also helpful to understand the traffic behavior of a day, starting from 0.00hr to 23.59hr. Accordingly, it was understood that the traffic behavior of a day more takes normal distribution behavior, in which the higher values cluster in the middle of the range and the rest taper off symmetrically toward either extreme. Figure 5: Actual Vehicle Count vs Sensor-Recorded Vehicle CountPlot © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 46 ( ) Year 2023 D Traffic Flow Forecast based on Vehicle Count Error Rate = | − | Actual Value × 100