Using Learning Algorithms In Synthetic Mobility Trace Generation

Traffic jams impact commuters every day. People waste time sitting in traffic, and emissions are also a factor to consider. It is known that making roads bigger and better does not solve congestion, but makes it worse. One of the solutions we can still apply is creating better policies for road use but testing them in the real world can be expensive and dangerous. Thus, we need better traffic simulation strategies that accurately depict reality. We propose traffic prediction by feeding real-world data to machine learning models. Our results show that it is possible with a high degree of confidence. Amongst the selected algorithms we evaluated, XGBoost displayed better performance, whilst fitting in an acceptable amount of time.