Knowledge and Situation-Aware Vehicle Traffic Forecasting

The deep learning revolution, driven especially by incredible achievements in image recognition technology, encourages a predominately data-driven approach to machine learning and data science techniques. However, the emphasis on statistical inference from big data sources alone has diminished the role of existing knowledge and well-established theory. For example, vehicle traffic patterns are highly regular on a weekly period but can deviate unexpectedly in certain situations such as inclement weather, accidents, road work, etc. A purely datadriven approach would simply ask the model to handle all the different situations, while knowledge and situation-aware systems will take advantage of existing knowledge to help guide the machine learning process. This work aims to illustrate how knowledge and situational awareness can help data scientists to build more effective models in the field of vehicle traffic forecasting. The special circumstances considered here include holidays, special weather conditions, accidents, and location awareness that facilitates transfer learning. In addition, we present a novel modeling technique, Quadratic Extreme Learning Machine, that generally improves upon the standard Extreme Learning Machine model while remaining relatively efficient. The Quadratic Extreme Learning Machine can be potentially used as an alternative to Neural Networks, which generally entails higher computational costs.