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Transport delays due to traffic jams are manifest in many urban areas worldwide. For the purpose of making road traffic networks more efficient, Intelligent Transport Systems (ITSs) are currently being developed and deployed. In order to mitigate (or even avoid) congestion, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication provide a means for cooperation and intelligent route management in transportation networks. In this thesis, the novel Predictive Congestion Minimization in combination with an A -based Router (PCMA ) algorithm is introduced, which provides a comprehensive framework for detection, prediction and avoidance of traffic congestion. It assumes utilization of Vehicle-to-Everything (V2X) communication for transmission of contemporary vehicle data such as route source and destination or current position, as well as for provision of routing advice for vehicles. By processing the vehicle data, an early congestion detection and subsequently the calculation of alternative routes becomes possible. This thesis quantifies the achievable improvements in travel time and fuel consumption, even if not all road users participate in the system.

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Backfrieder, Christian
Dr. Christian Backfrieder, BSc MSc: studied Mobile Computing Bachelor´s and Master´s degree at the FH Upper Austria; PhD at the Faculty of Electrical Engineering and Information Technology, TU Vienna; software engineer at Magna Powertrain, St. Valentin, Austria.

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