Study On Automatic Detection Algorithm For Traffic Incident Using Neural Network Based On Rough Set Filtration

With the development of highway and traffic problem being serious day by day inbig cities, the demand of establishing Emergence Management Systems (EMS)becomes urgent. Effective detection for traffic incident is the first step and the keycomponent of operating the EMS successfully;therefore, the study on detectionalgorithm for traffic incident has become a hot issue. The study tries to combine therough set theory and neural network to establish a new automatic detection algorithmfor traffic incident using neural network based on rough set filtration. Main researchwork is listed as follows:The existing automatic detection algorithms for traffic incident are summarized,the performances of it are compared and analyzed, the index for evaluatingalgorithm's performance is listed,and then the basic principle of the algorithm in thethesis is expounded.A number of problems in data mining using rough set had been studied deeply inthe thesis,for example, feature selection, consecutive feature discretization, seekingthe optimum deduction algorithm. Several algorithms using neural network to detecttraffic incident also had been introduced and compared. The structure's selection andtraining of neural network were studied in particular, which were the basic ofestablishing the algorithm in the thesis.A new automatic detection algorithm for traffic incident using neural networkbased on rough set filtration was proposed, which has the advantages of neuralnetwork and rough set theory. Neural network has several characteristics, such asdisposing datum in parallel, aiming for overall function, storing informationdistributed and so on, which can produce a non-linear map by training and learning,cluster data adaptively, with the abilities of restraining the noise's disturbance andgood robustness. Its shortcomings are that when the space dimension of the inputinformation is larger, not only complex the network's structure is, but also the networkneed more time to train. Rough set can deduct feature and value of the data,eliminatethe noise and redundant targets in sample. The merge not only reduced the scale of thenetwork, lessened burden of training and learning by eliminating redundant targets,but also improved the accuracy of detection by eliminating noise. The network in thethesis has multi-layer feed forward architecture;the motion vector and the adaptivelearning rate are also adopted for better performance.In order to verify the effect of the algorithm proposed in the thesis, simulationmodel of traffic incident was established to obtain the traffic data which was used totrain and test the algorithm. Comparing and analyzing had been done among thealgorithm in the thesis, the existing neural network and traditional detectionalgorithms. The results show that the algorithm in the thesis has higher detection rateand lower false detection rate. Its coordination ability to evaluation index is alsobetter than other algorithms.