Spatial-temporal sensor data imputation in traffic data modelling
The traffic data corrupted by missing data significantly limit the robustness of traffic modelling. Our research aims to develop modelling techniques for data imputation tasks. Our first work is to impute speed data by an RBF based fitting approach for multivariate data matrixes with irregular locat...
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| Format: | Thesis-Master by Research |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/167860 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The traffic data corrupted by missing data significantly limit the robustness of traffic modelling. Our research aims to develop modelling techniques for data imputation tasks. Our first work is to impute speed data by an RBF based fitting approach for multivariate data matrixes with irregular location graphs. The results show that RBF approach is capable of imputing missing values, especially in a short missing span. By incorporating the available dimensions of the data, RBF method can utilize the information from time and space dimensions for effective imputation with higher missing ratios. Our second work is to propose a deep learning imputation model with a Stacking STGCN Auto-Encoder structure. We develop an adaptive graph convolution technique for to effectively utilize data missing status when traffic data suffer from severe missing conditions. The experiments show promising results when the traffic data contain large random missing regions in the time domain. |
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