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Using Real-Time Traffic Data for Transportation Planning

机译：将实时交通数据用于交通规划

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This research report, the state-of-the-art and the state-of-the-practice related to using real-time traffic data are reviewed first and the needs of ITS data for planning purposes are identified. Then, an optimization process that can provide the optimized aggregation level of ITS data for different applications is developed. To illustrate the wavelet algorithm based technique, ITS data archived by the TransGuide center in San Antonio is used for case study. Aggregation levels for different days of a week and different time periods over the whole year of 2001 are obtained by the proposed approach. Subsequently, an optimization-based sampling approach for data archiving is presented. This data archiving approach can identify the best representative samples of the raw ITS data based on either sum squire error (SSE) or cross validation (CV) while minimizing the required storage size. The sampling approach is realized through a data processing procedure, which is designed to archive real-time/raw data, aggregated data, sampled data, as well as extension factors which can be generated from the raw data. It was tested also in the case study of TransGuide of San Antonio, Texas, where real-time data were collected from 527 loop detectors. After the proposed sampling approach was applied in the case study, only one tenth of the original data is needed to be stored, while the resulting optimal samples contain the maximum information of the raw data, which are able to meet the potential uses of various transportation purposes.

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