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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Published in:2019 IEEE Intelligent Transportation Systems Conference (ITSC) pp. 3482 - 3487
Main Authors: Wu, Jianqing, Zhou, Luping, Cai, Chen, Dong, Fang, Shen, Jun, Sun, Geng
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2019
Subjects:
Delays
GTFS
Long Short-Term Memory
Machine learning
Prediction
Prediction algorithms
Predictive models
Primary Delay
Rail transportation
Railways
Schedules
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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.
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
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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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StartPage 3482
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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