The Research And Application Of Time Series Data Mining To The Urban Road Safety

Through analyse and research the existing time series datas, time series data miningcan discover and extract implicit in the time series which can not be visually embodiedpotentially useful information and knowledge. That not only uses the huge original dataseries secondary, but provides a guide to the followed work with useful pattern or theknowledge. There are a lot of unknown patterns within the large amounts of raw trafficdata that is generated by the urban intelligent traffic information system; the data can beused in urban road safety management. In order to effectively use these data, we proposea method of intra-area traffic flow patterns discovery based on similarity and a method ofprediction on driving track of specific vehicles in potential group on the monitoring areaand specific vehicle side. Finally, this paper achieves the design and implementation ofurban road safety analysis system combines WPF (Windows Presentation Foundation)that has the ability to provide more support in urban road safety.Following works have been done in the paper:1. This paper introduces the development of time series mining technology ondomestically and abroad, the application of data mining technique in intelligenttransportation systems, the state of urban road safety analysis research. It can beused to protect the urban road safety.2. This paper sets up a strict verification method for the features of time-series datato verify the quality of eight useful similarity measurement methods, andestablishs DTW technology which is best suited for describe the similarityrelations of time sequence. It provides references for the selection of similaritymeasurement methods.3. Aiming the original DTW technical computing overhead is too big this problem,we propose a new lower bound function calculation method. And on this basis,proposed a discovery method based on improved lower bound function specificpattern similarity search and a periodic pattern discovery method based onimproved lower bound function clustering technology that combine intra-areatraffic flow patterns discovery. Finally, the methods we built for experimentaltest verify the effectiveness and practicality of the proposed algorithm.4. This paper proposes a method of prediction on driving track of specific vehicles in potential group. In this method, we leverage the proposed searching algorithmto potential group of specific vehicles and sequential pattern discovery andBayesian networks complementary predictable pattern, effectively addressingthe two big problems of the most concerned that both the potential group findingand driving track in the current urban road safety, in order to assure that thereliable technological means provided in urban road safety.5. This paper sets up an urban road safety analysis System which reliability,stability and extendibility are guaranteed by some Techniques such as WPFTechnique.