Predictive Modelling of Traffic Flow with Deep Learning Techniques

Abstract

This project delves into predictive modeling for traffic flow using deep learning techniques, focusing on the Metro Interstate dataset. Traffic Flow Prediction (TFP) is crucial for Intelligent Transport Systems (ITS), optimizing vehicle movement, reducing congestion, and improving route efficiency. Leveraging advancements in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Big Data, our study explores various techniques and models for TFP. We highlight DL models' advantages over traditional ML methods, propelled by the wealth of real-time traffic data fostered by smart cities, presenting opportunities to craft robust predictive models. The core of our project revolves around developing a multi-step Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) architecture. Our model forecasts traffic volume between Minneapolis and St. Paul, Minnesota, predicting volume two hours into the future based on a six-hour historical window. We explore DL algorithms' efficacy, including LSTM and Gated Recurrent Unit (GRU), in mitigating challenges like the vanishing gradient problem common in RNNs. Our analysis compares various NN models, emphasizing the importance of data availability for training and fine-tuning ML/DL models, with the Metro Interstate dataset serving as a crucial asset for comprehensive traffic flow analysis and model development.