An approach using machine learning and public data to detect traffic jams

With the growth of large cities, it becomes necessary to overcome several challenges related to infrastructure. In this context, urban mobility, including transportation systems, is a key topic. It is known that more efficient traffic management makes cities more efficient as a whole, improving the quality of life of their residents and tourists. A common problem that arises concerns traffic jams, encompassing their detection and management. With proper traffic jam detection, it is possible to implement traffic management methods to prevent long-term occurrences or even notify drivers to avoid specific locations. Some traffic jam detection solutions use sensors spread across cities, while others are based on sensors in drivers' smartphones. These approaches have limitations related mainly to the information reliability or to the cost and complexity of implementation. In this work, we propose an approach using Machine Learning techniques applied to public data of transportation systems to detect urban traffic jams. The proposed approach is evaluated using the publicly available GPS data from the buses of the Rio de Janeiro/Brazil transportation system and traffic jam events posted on Twitter by the Rio de Janeiro City Hall.