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

Figure 4: Flow Chart of the Vehicle Detection Algorithm Then, sensor accuracy was tested by plotting the actual vehicle counts vs sensor detected vehicle counts graph considering 200 data points of that data entries. Similarly, the accuracy of the sensor was also measured as a percentage to ensure the sensor's performance. b) Neural Network Training and Forecasting Stage This stage includes training LSTM neural network model. To accomplish this task, data preparation and segmentation were carried out before sending to the neural network model for training. The data gathered to Thing Speak was exported as. csv files to train the LSTM model. During the data consolidation process, .csv files gathered from Thing Speak were modified separating date and time into two columns, removing the entry ID column, and adding missing entries. Similarly, the values which imply significant deviations were removed from the data set. Those modified .csv files were fed into the system as inputs by dividing them into four segments. And the input data volume that is taken to train the model andwindow size were used as variable parameters to find the bestcase of the prediction model. Since the vehicle count is taken in five minutes intervals, In one hour: 12 data points In one day: 12 × 24 = 288 data points In a week: 12 × 24 × 7 = 2016 data points | In a month: 12 × 24 × 30 = 8640 data points Likewise, data gathered over five weeks by monitoring the vehicular traffic were employed to create the forecasting models. Then the entire data collection was separated into two setsfor training purposes and testing purposes. Eight thousand sixty-four data points (data gathered over a duration of a month) were employed as the training data set while the remaining set of 2016 data points (data gathered over a week) were used as test data for the model testing purposes. The entire training data set was again split into four segments as week 1, week 2, week 3 and 4. Then obtained data patterns over a day and week were observed to identify the patterns of the traffic behavior. After that more than ten separate models were tested to find the besttraffic prediction model. Initially, the first set of models were created by setting the window size to 12 while changing the input data volume from 2016 data points to 8064 data points. When the 2016 input data set is considered, it includes the datagathered © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 45 ( )D Year 2023 Traffic Flow Forecast based on Vehicle Count