Towards a General Prediction System for the Primary Delay in Urban Railways
Nowadays a large amount of data is collected from sensor devices across the cyber-physical networks. Accurate and reliable primary delay predictions are essential for rail operations management and planning. However, very few existing `big data' methods meet the specific needs in railways. We p...
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| Vydané v: | 2019 IEEE Intelligent Transportation Systems Conference (ITSC) s. 3482 - 3487 |
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| Hlavní autori: | , , , , , |
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| Jazyk: | English |
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01.10.2019
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| Abstract | Nowadays a large amount of data is collected from sensor devices across the cyber-physical networks. Accurate and reliable primary delay predictions are essential for rail operations management and planning. However, very few existing `big data' methods meet the specific needs in railways. We propose a comprehensive and general data-driven Primary Delay Prediction System (PDPS) framework, which combines General Transit Feed Specification (GTFS), Critical Point Search (CPS), and deep learning models to leverage the data fusion. Based on this framework, we have also developed an open source data collection and processing tool that reduces the barrier to the use of the different open data sources. Finally, we demonstrate an advanced deep learning model, the novel ConvLSTM Encoder-Decoder model with CPS for better primary delay predictions. |
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| AbstractList | Nowadays a large amount of data is collected from sensor devices across the cyber-physical networks. Accurate and reliable primary delay predictions are essential for rail operations management and planning. However, very few existing `big data' methods meet the specific needs in railways. We propose a comprehensive and general data-driven Primary Delay Prediction System (PDPS) framework, which combines General Transit Feed Specification (GTFS), Critical Point Search (CPS), and deep learning models to leverage the data fusion. Based on this framework, we have also developed an open source data collection and processing tool that reduces the barrier to the use of the different open data sources. Finally, we demonstrate an advanced deep learning model, the novel ConvLSTM Encoder-Decoder model with CPS for better primary delay predictions. |
| Author | Shen, Jun Cai, Chen Dong, Fang Sun, Geng Wu, Jianqing Zhou, Luping |
| Author_xml | – sequence: 1 givenname: Jianqing surname: Wu fullname: Wu, Jianqing organization: University of Wollongong,School of Computing and Information Technology,Wollongong,Australia – sequence: 2 givenname: Luping surname: Zhou fullname: Zhou, Luping organization: University of Wollongong,School of Computing and Information Technology,Wollongong,Australia – sequence: 3 givenname: Chen surname: Cai fullname: Cai, Chen organization: Data 61, CSIRO,Advanced Data Analytics in Transport,Sydney,Australia – sequence: 4 givenname: Fang surname: Dong fullname: Dong, Fang organization: Southeast University,School of Computer Science and Engineering,Nanjing,China – sequence: 5 givenname: Jun surname: Shen fullname: Shen, Jun organization: University of Wollongong,School of Computing and Information Technology,Wollongong,Australia – sequence: 6 givenname: Geng surname: Sun fullname: Sun, Geng organization: University of Wollongong,School of Computing and Information Technology,Wollongong,Australia |
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| Snippet | Nowadays a large amount of data is collected from sensor devices across the cyber-physical networks. Accurate and reliable primary delay predictions are... |
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| SubjectTerms | Delays GTFS Long Short-Term Memory Machine learning Prediction Prediction algorithms Predictive models Primary Delay Rail transportation Railways Schedules |
| Title | Towards a General Prediction System for the Primary Delay in Urban Railways |
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