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Travel time forecasting is an important content of dynamic traffic navigation. Dynamic traffic data collection is the precondition of forecasting. Many traffic data collection methods have been adopted, such as loop inductive vehicle detector, radar detector, video detector, GPS floating car and so on. Due to the widely distribution, GPS floating car has become the most efficient mean to collect instantaneous...
The realization of the Intelligent Transportation Systems will effectively solve the problem of traffic congestion and urban traffic pollution, improve the road capacity and traffic safety. A crucial key of the realization of the ITS is the estimate and prediction of travel time: how to make and continuously update prediction of travel time for several minutes into the future using real-time data...
With the increasing of vehicle quantity, traffic violations occur frequently. It is an efficient means to avoid traffic accident through controlling traffic violation. Based on the study of traffic accidents, black spot of traffic violation can be determined. But little research work has been done on this subject at present. This paper proposed a traffic violation black spot analysis method. In the...
It is a crucial part for ATMS to accurately identify and forecast traffic state from real-time traffic data. To improve the identification rate of traffic state, multisource information should be used. The multisource information fusion method is important. Information fusion is divided into three levels, i.e. data level, feature level, and decision level. In traffic congestion identification, many...
According to the feature of software in bioinformatics field, this paper proposes a suite of software integration solution based on grid service - SoSIS. In the method, other solutionspsila ideas are used for referenced, such as PISE, myGrid and VINCA. Firstly, bio-software is encapsulated into grid service. Secondly, the grid service is abstracted into business service. Thirdly, the business service...
Based on current work about high order Boltzmann machine (BM) and unsupervised BM, an unsupervised learning algorithm based on high order BM is proposed. It is different from supervised BM in that it has no training samples for output units. In the unsupervised BM, the maximization of the mutual information based on Shannon entropy is used as an unsupervised criterion. As we all know, the computation...
Based on current work about high order Boltzmann Machine (BM) and unsupervised BM, an unsupervised learning algorithm based on high order BM is proposed. It is different from supervised BM in that it has no training samples for output units. In the unsupervised BM, the maximization of the mutual information based on Shannon entropy is used as an unsupervised criterion. As we all know, the computation...
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