Real-Time Traffic Congestion Detection for Driver-Centric Applications

With the increasing time drivers spent behind the wheel, availability of accurate, reliable, and comprehensive information on real-time traffic conditions is becoming increasingly important for individual road users, industry, and traffic authorities. Due to a growing amount of traffic combined with insufficient transportation infrastructure, traffic congestion is one of the major challenges modern society has to face in urban areas. While this results in serious environmental issues such as pollution, congestions also have significant effects on drivers' stress levels, which further is an essential factor in driver behavior and road safety. In this paper, we analyze real-time traffic situations on a fine-granular level by computing the local congestion factor, which represents traffic conditions on currently traversed route segments. We show that travel time predictions for short- and mid-term routes provided by four major public traffic information providers can be used for computing congestion factors in real-time, which can then be used as input for driver-centric applications.