TRAFFIC CONGESTION PREDICTION USING MULTI-LAYER PERCEPTRONS AND LONG SHORT-TERM MEMORY

• Main Author: Sena Musa Satria, Ahmad
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/46206
• Institution: Institut Teknologi Bandung

Traffic road congestion prediction is one of the solutions for solving the high rate of traffic congestion in many parts of the world. Various studies have been done in traffic congestion prediction, however most of them vary in data, context and method. This study explores the usability of CCTV footage to perform traffic prediction. The footage is processed automatically using object detection and object tracking algorithm to obtain traffic data. After that, the traffic data is modeled using both Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM). Model performance is measured using Root Mean Squared Error (RMSE) to get best approximation of the data. This study prove that automatically processed CCTV footage is indeed a viable option for traffic congestion prediction. The best model achieved RMSE value of 1.88, using MLP method and amounts of cars, buses and trucks as predicted variable.