Research And Application On Intelligent Transportation System Data-driven

With the rapid development of economic society, the city scale expandsunceasingly, urban traffic is faced with great pressure. In order to reduce trafficaccidents, solve the traffic congestion and promote the city environmental protection,improve the intelligent transportation system construction is the basic way to improvepeople’s quality of life, which can improve the level of traffic management and serviceof information and decision support capabilities. Traffic data collection system is animportant part of intelligent transportation system, its purpose is to use informationidentification technology to improve the traffic administrative department of highwaytransportation situation decision, and to determine the effectiveness of thedecision-making, provide data basis.However, along with the further research of intelligent transportation system, andfurther found that, for the construction of transportation as a complex system, the objectof study also become increasingly complex. Individual traditional method based onprecise mathematical model has been very difficult, and establish the model ofportability restricted by model assumptions. Besides, the use of a large number ofsensors and detectors, make every day to process a large number of data in the database.In the face of these vast amounts of offline and online data, the existing research methodbased on the model has been compromised So, Data-driven idea and technology in sucha background was introduced to the field of intelligent transportation.According to the above problem, this paper through the demand analysis todetermine the logic framework of the intelligent transportation system. From the logicalframework that data accuracy, effectiveness is the foundation of the intelligenttransportation system. Through analysis summarizes the advantages and disadvantagesof data mining technology in intelligent transportation system, and the characteristics ofthe noise data and fault data. Combined with data driven thoughts and technology, theintelligent transportation system logic framework of each level, namely, datapreprocessing, data fault diagnosis and application of three aspects are studied. Themain research contents are as follows:1, the data noise reduction model based on datadriven research. The model in the service of fault diagnosis, which is an improvedwavelet threshold noise cancellation algorithm for modeling.2, the fault diagnosis datamodel based on data driven research. The model is used for real-time monitoring of traffic detection data, the data of the failure of separation. Analysis of the faultinformation at the same time, in order to judge the failure of equipment, and guaranteethe validity of the data to provide data basis and equipment maintenance. The modelwith the improved MSPCA algorithm, in this paper, with other similar algorithms, suchas PCA, adaptive PCA and classic MSPCA compares the simulation results.3,automatic judgment model based on data driven research. The model adopts the pilotstudy of the two models after processing the data. Use condition proof theory modeling,at last the verification experiment is designed. Results show that the model accuracy ishigher, judging its theory can be used in road congestion, and less modelassumptions.So it can be the intersection position limits, portability strong.