How Case-based Reasoning can be used to predict and improve Traffic Flow in Urban Intersections.
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The traffic situation in urban areas has become a large problem over the recent years. Especially congestion during rush hours is becoming quite visible. Often, this can not be solved by simply building more roads, as most urban areas do not have the available physical space required. One way of solving these problems can be to focus on decreasing the number of vehicles. This can be done by increasing the amount of people traveling in each vehicle, or focusing on alternative transportation such as bicycle or public transport. Another way of utilising the existing infrastructure, is to improve the control of the traffic flow. In this thesis we present a prototype case-based reasoning (CBR) system for the purpose of controlling the traffic lights in an urban intersection. The system uses historical vehicle counts, obtained from an intersection in the city of Trondheim, before using this knowledge in order to make new signal plans for the intersection. jCOLIBRI is used as framework for the development of our CBR-system and evolutionary algorithms are used for weighting the case base. The traffic simulator Aimsun is used for the evaluation of our solution. Simulation results indicate that the CBR-system is able to satisfactory calculate signal plans based on the predicted traffic, in a variety of different scenarios.