Traffic Prediction Using Python Tkinter Gui & Sumo

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Time-series AI models for traffic congestion prediction Summary : Predicting congestion in traffic early is key to solving many real life challenges, e.g., avoiding routes with congestion for food delivery, reaching on time for meetings and football games, improved estimates of travel time etc. Many on-road factors contribute to a future traffic congestion, e.g., road quality, non-recurring events such as accidents, driver behaviour on road and historic data. We will be developing time-series AI models for early prediction of traffic congestion. We will explore state-of-the-art deep learning techniques such as Long Short-Term Memory networks, Transformer networks, as well as recent novel Factored Latent-Dynamic Conditional Random Fields for such task. There is room for novel approaches in considering different factors leading to congestion, as well as developing new AI techniques for such application. The student must have interest in machine learning, AI and/or routing and optimization areas. Strong coding skills in Python is a bonus. For this project, no fix solution or flow, any deep learning techniques can be implemented as long as traffic prediction results shows that there is an improvement in accuracy(%) results obtained. Using different models and compare to baseline & see which model can perform optimised prediction accuracy/shows an increase in accuracy(%) Required for my project: Diff code files (inclusive of comment lines), data, results obtained after preprocessing, training process etc, graphs for diff model used for making comparison to see which model can best perform accurate prediction (%)., graphs, screenshots of road network which you think is impt to note to put in the report writing for later part which I will do, I just need the impt results,data, pictures etc for me to explain & elaborate on the method and understanding of the method flow. Tools needed to be used: tkinter GUI python, SUMO for traffic modeling, Jupyter for plotting of graphs, tables?

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