The method was studied about traffic flow prediction by using subtractive clustering for fuzzy neural network model of phase-space reconstruction. The prediction model of traffic flow must be established to satisfy the intelligent need of high precision through analyzing problems of the existing predicting methods in chaos traffic flow time series and the demand of uncertain traffic system. Based on the powerful nonlinear mapping ability of neural network and the characteristics of fuzzy logic, which can combine the prior knowledge with fuzzy rules, the knowledge base of the traffic flow predicting system was established by using fuzzy neural network model based on subtractive clustering. Subtractive clustering generates the number of fuzzy rules and the clustering centers are regarded as the initial training parameters of the predicting modeling. The predicting model of fuzzy neural network can be quickly trained online. Genetic algorithm was used in determining the clustering radius. The simulation result shows its correctness and feasibility.
The real-time recognition problem of chaos in traffic flow was studied using reduced support vector machine. Based on analyzing the demand of intelligent transportation system and the problems of the exiting recognition methods, the intelligent real-time recognition method of chaos in traffic flow was proposed. The principle and the structure of the system are briefly introduced. There are online recognition subsystem and offline recognition subsystem mainly. Normal methods are used in the offline recognition model. The online recognition model was established using reduced support vector machine, which the wavelet packet energy features vector of the anterior time series of traffic flow in every training samples were used as input variables. The reduced samples are treated as the final training sample. When the new samples are appended, the reduced set may be updated. The rules of models can be updated and the problem of large sample size could be solved. The simulation result shows that the method is correct and feasible. And it can satisfy the real-time requirement of chaos recognition.
Based on the symmetric two-lane Nagel-Schreckenberg (STNS) model, a three-lane cellular automaton model between two intersections containing a bus stop with left-turning buses is established in which model the occurrences of vehicle accidents are taken into account. The characteristics of traffic flows with different ratios of left-turn lines are discussed via the simulation experiments. The results indicate that the left-turn lines have more negative effects on capacity, accident rate as well as delay if the stop is located close to the intersections, where the negative effect in a near-side stop is more severe than that in a far-side one. The range of appropriate position for a bus stop without the bottleneck effect becomes more and more narrow with the increase of the ratio of left-turn bus lines. When the inflow is small, a short signal cycle and a reasonable offset are beneficial. When the inflow reaches or exceeds the capacity, a longer signal cycle is helpful. But if the stop position is inappropriate, the increase of cycle fails in reducing the negative effect of left-turning buses and the effectiveness of offset is weakened.
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