Traffic Route Guidance using Feedback of Predicted Travel Times : Improving Travel Times in the Berlin Traffic Network

Abstract: Traffic congestions constitute a problem in many large cities. Congestions can be handled by reducing the network demand, expanding the infrastructure, or by utilizing the road network more efficiently. This master thesis presents a methodology for route guidance, based on automatic feedback control from the current traffic situation. Through variable direction signs or individual in-car devices, all vehicles with a certain origin and destination (which are both normally intermediate) are guided to take the currently fastest route. In this paper the traffic is guided over one of two alternative routes with the goal of a Nash equilibrium, i.e. that every guided agent travels the fastest path. Nash equilibrium occurs when the routes have equal travel times, or when all agents use a route with shorter travel time. Predictive data describing how the system reacts to control measures is fundamental to control the traffic in an optimal way. Feedback of observed travel times results in a system with highly oscillating travel times. Given this background, the task of this thesis has been to present a model that predicts route travel times, with the purpose of improving the performance of traffic route guidance. The approach used is automatic feedback control, and therefore some basic terminology from control theory is used throughout the report. The model introduced in this paper needs no parameter estimation but uses only static information about traffic network and on-line counting of vehicles. Simulations show that with reliable travel time predictions at hand, optimal control can be achieved by bang-bang control; a controller that needs no parameter estimation. The result is a guidance method that has the potential to work on any location without prior estimation of location specific parameters. A microscopic traffic simulator, MATSim, is used for developing the prediction model and evaluating its system effect with route guidance. Simulated in-car COOPERS (Co-operative Systems for Intelligent Road Safety) devices are used for data collection and for transmitting the guidance to vehicles. The prediction models and the feedback control are evaluated in two different traffic networks; a topologically simple test network and a realistic location in the Berlin network. The results of the simulations are promising; guidance with predictive models results in significantly shorter average travel time than guidance based on observed travel times. Evaluation with an economic measure indicates that drivers benefit economically from predictive route guidance