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

IV. M ethodology The methodology is introduced by being split into two main sections: the traffic monitoring stage and the forecastingstage using neural networks emphasizing the main milestonesof the project. a) Traffic monitoring stage Vehicle detection was accomplished using a TFMini Plus LiDAR sensor installed by the side of the road, to monitor the vehicles directed to one side, determining the desired tilt angle and height. Sensor positioning is illustrated in below figure 3. TFMini Plus sensor’s Time of Flight (ToF) principle is used to detect the presence of vehicles. The sensor's periodically emitted modulation waves are used to detect andcalculate the proximity to the object and its time-of-flight is estimated by measuring the round-trip phase difference of its reflection when it contacts an object [49]. These periodic modulation waves are always set to be directed towards the road and to be released at a frequency of 16.667 Hz with 60- millisecond intervals. Universal Asynchronous Receiver / Transmitter (UART) communication was used to communicate with the microcontroller. Distance limitations for the sensor were configured as the distance should be between the range of 800 to 1220 meters considering white lines which indicate the margins and use to separate traffic traveling in the same direction, assuring the sensor is not triggered for pedestrian movements or the vehicles in the other direction. Additionally, distance measurements must persist longer than 50 milliseconds and return to zero which is the initial distance value. The presence of a vehicle will therefore be counted as present if both prerequisites are met, where the count will be one. Likewise, the vehicle count is taken and increased within a 5-minute interval. And the vehicle count isprogrammed to be zero after every 5 minutes (5 ×1000 × 60 milliseconds) intervals and the latest value which is recordedas the vehicle count inside that interval is to be delivered to the ThingSpeak IoT platform as well as to an SD card. Here the ThingSpeak online IoT platform was used as the source to create the database. A database was developed by recording consecutively collecting data. ESP8266 (NodeMCU) modulewas used as the microcontroller which can enable further improvements with IoT connecting several nodes. Figure 3: An Illustration of a Subspace Without (Left) Or With(Right) a Vehicle [50] Prediction Model Prediction Models Comparison Strengths Weaknesses • Predict traffic flow in proportion • to road conditions. Fuzzy Model • Low Prediction Error. Simplicity. • High Accuracy. • Poor functionality amid the noise and other disturbances. • High dependency on recorded data. KNN Model • High Accuracy. • Predict traffic flow in proportion to road • Poor functionality amid the noise and other disturbances. • High © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 44 ( ) Year 2023 D Traffic Flow Forecast based on Vehicle Count

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