MISSING DATA TREATMENT AND DATA FUSION TOWARD TRAVEL TIME ESTIMATION FOR ATIS

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概要

• 論文の詳細を見る

• This study develops a travel time estimation process by integrating a missing data treatment and data-fusion-based approaches. In missing data treatment, this study develops a grey time-series model and a grey-theory-based pseudo-nearest-neighbor method to recover, respectively, temporal and spatial missing values in traffic detector data sets. Both spatial and temporal patterns of traffic data are also considered in travel time data fusion. In travel time data fusion, this study presents a speed-based link travel time extrapolation model for analytical travel time estimation and further develops a recurrent neural network (RNN) integrated with grey models for real-time travel time estimation. In the case study, field data from the national freeway no. 1 in Taiwan is used as a case study for testing the proposed models. Study results showed that the grey-theory-based missing data treatment models were accurate for recovering missing values. The grey-based RNN models were capable of accurately predicting travel times. Consequently, the results of this study indicated that the proposed missing data treatment and data fusion approaches can ensure the accuracy of travel time estimation with incomplete data sets, and are therefore suited to implementation for ATIS.

• Eastern Asia Society for Transportation Studiesの論文

著者

• CHO Hsun-Jung

  Department of Transportation Technology and Management National Chiao Tung University

• WEN Yuh-Horng

  ITS Research Center Department of Electrical and Control Engineering National Chiao Tung University

• LEE Tsu-Tian

  Department of Electrical and Control Engineering National Chiao Tung University

関連論文

• MISSING DATA TREATMENT AND DATA FUSION TOWARD TRAVEL TIME ESTIMATION FOR ATIS
• THE BEHAVIOR ANALYSIS ON A NEW TYPE WEAVING SECTION

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