Title: New Approaches for Online Control of Urban ...
Publisher: Shaker Verlag Mai 2011
Publication Date: 2011
Book Condition: Neu
Neuware - Traffic signal control is one of the key factors in urban traffic control. It directly influences the quality of traffic flow. Besides fixed time and vehicle-actuated control, so called Adaptive Traffic Control Systems (ATCS) have been developed in the past. These systems control a set of connected intersections in a network and aim at optimizing traffic signal control in real-time by continuously adapting the signalization at the intersections to the currently detected or estimated traffic demand in the network. Improvements of traffic modeling techniques and of computer power promote further development of such sophisticated systems. In this thesis a new ATCS prototype has been developed and evaluated. The development has been motivated by recent research. Based on a comprehensive overview of the state-of-the-art of science and technology of traffic signal control the remaining need for research has been derived, followed by an overview of the conceptual design of the new ATCS prototype. The prototype employs a centralized concept and uses an optimization interval of 15 minutes, i.e. every quarter of an hour signal timings of all signalized intersections are optimized on a central computer and sent to the local controllers where the traffic signals are controlled accordingly. The first major task that has to be performed at each interval is to estimate the traffic demand of the next optimization interval. It is assumed that all lanes at all signalized intersections are equipped with vehicle detectors. Based on the detector counts of previous time intervals and on reference traffic demand patterns, a forecasting module estimates the detector counts of the next interval. In a next step, these counts are used as constraints for the estimation of the overall traffic demand in the whole network. This demand comprises Origin-Destination flows, traffic volumes on different routes and on all links of the network. The demand estimation module is based on previous research which built on information theory. The method has been implemented and further generalized to be applicable in the framework of an ATCS. After application of the two modules, an estimate of the upcoming traffic demand in the whole network is available which can be used for optimization of signal settings. The next two modules of the ATCS perform an adjustment of cycle length and phase durations and an optimization of offsets. The first is done by implementing classic formulas for the calculation of fixed time signal plans. The module calculates a network-wide common cycle length in order to enable coordination of the intersections. The main focus of the adjustment of signal settings to the currently estimated traffic demand has been on the model-based offset optimization. Offset optimization aims at establishing a good coordination of adjacent intersections in such a way that vehicles do not have to stop at each intersection but can travel in so called green bands. A macroscopic traffic flow model has been used to evaluate the effects of different offset combinations in terms of total delay. For each offset combination to be tested, a single run of the model has to be performed. Different optimization algorithms have been implemented, thereof two based on heuristic Genetic Algorithms. A third, deterministic algorithm has been developed in addition. The last major object of research of this thesis was signal plan transition. At the beginning of each time interval, the new signal timings have to be implemented at each intersection. This requires application of an appropriate technique of signal plan transition that does not induce major disturbances of traffic flow. Based on the state-of-the-art of signal plan transition and on an additional simulation study a rather smooth transition technique has been identified which has been implemented in the framework of the ATCS prototype. It has been incorporated into the model-based offset optimization in order to c. Bookseller Inventory # 9783832298623
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