In traditional intelligent transportation system (ITS), the information source is often collected from a single view. However, as ITS becoming increasingly complicated, there is a need to describe one object from several different information views/domains. Information from multiple sources can provide extra worthwhile information for ITS users especially in cloud environment. Moreover, existing local ITSs usually provide information by fixed algorithms, which is not adequate to the dynamic transportation scenarios that produce big traffic data with time series. In this paper, we propose a complete self-adaptive multi-view framework for multi-source information service in cloud ITS, which mainly consists of a Newton multi-parameter optimization, a multi-layer feed-forward neural network and a finite multi-view mixture distribution. A simulation on real-world application, with six different types of information views, demonstrates the underlying effectiveness of the proposed framework.
['Shan Xue', 'Li Xiong', 'Shufen Yang', 'Lu Zhao']
A self-adaptive multi-view framework for multi-source information service in cloud ITS