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- Publisher Website: 10.1109/ITSC.2019.8917286
- Scopus: eid_2-s2.0-85076810057
- WOS: WOS:000521238102052
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Conference Paper: Public Transport Waiting Time Estimation using Semi-Supervised Graph Convolutional Networks
Title | Public Transport Waiting Time Estimation using Semi-Supervised Graph Convolutional Networks |
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Authors | |
Issue Date | 2019 |
Publisher | IEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000396 |
Citation | 2019 22nd Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 October 2019, p. 2259-2264 How to Cite? |
Abstract | An effective transportation system is important for supporting various human activities in a modern smart city. The waiting time at various stations has great impacts on the overall transportation system efficiency and people’s health like stress and anxiety. Knowing the waiting time at different locations in advance can assist the travelers to plan their trips. However, such waiting time may depend on many factors like crowdedness and the collective travel behaviors of the travellers involved. In general, it is very expensive to collect all the required data at every location. In this paper, a deep learning approach is proposed for determining the waiting time levels at public transport stations based on some proxy data and limited historical waiting time data at some stations. We formulate the public transportation network as a graph and develop a semi-supervised classification model based on Graph Convolutional Networks which can operate directly on the graph-structured data with limited labelled data. We conduct experiments for the mass transit railway in Hong Kong with real data and our proposed approach can achieve 89% accuracy of classifying the waiting time levels. |
Persistent Identifier | http://hdl.handle.net/10722/280991 |
ISBN | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | CHU, KF | - |
dc.contributor.author | Lam, AYS | - |
dc.contributor.author | Loo, BPY | - |
dc.contributor.author | Li, VOK | - |
dc.date.accessioned | 2020-02-25T07:43:41Z | - |
dc.date.available | 2020-02-25T07:43:41Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | 2019 22nd Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 October 2019, p. 2259-2264 | - |
dc.identifier.isbn | 978-1-5386-7025-5 | - |
dc.identifier.uri | http://hdl.handle.net/10722/280991 | - |
dc.description.abstract | An effective transportation system is important for supporting various human activities in a modern smart city. The waiting time at various stations has great impacts on the overall transportation system efficiency and people’s health like stress and anxiety. Knowing the waiting time at different locations in advance can assist the travelers to plan their trips. However, such waiting time may depend on many factors like crowdedness and the collective travel behaviors of the travellers involved. In general, it is very expensive to collect all the required data at every location. In this paper, a deep learning approach is proposed for determining the waiting time levels at public transport stations based on some proxy data and limited historical waiting time data at some stations. We formulate the public transportation network as a graph and develop a semi-supervised classification model based on Graph Convolutional Networks which can operate directly on the graph-structured data with limited labelled data. We conduct experiments for the mass transit railway in Hong Kong with real data and our proposed approach can achieve 89% accuracy of classifying the waiting time levels. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000396 | - |
dc.relation.ispartof | IEEE International Conference on Intelligent Transportation Systems (ITSC) | - |
dc.rights | IEEE International Conference on Intelligent Transportation Systems (ITSC) . Copyright © IEEE. | - |
dc.rights | ©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.title | Public Transport Waiting Time Estimation using Semi-Supervised Graph Convolutional Networks | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Lam, AYS: ayslam@eee.hku.hk | - |
dc.identifier.email | Loo, BPY: bpyloo@hku.hk | - |
dc.identifier.email | Li, VOK: vli@eee.hku.hk | - |
dc.identifier.authority | Lam, AYS=rp02083 | - |
dc.identifier.authority | Loo, BPY=rp00608 | - |
dc.identifier.authority | Li, VOK=rp00150 | - |
dc.identifier.doi | 10.1109/ITSC.2019.8917286 | - |
dc.identifier.scopus | eid_2-s2.0-85076810057 | - |
dc.identifier.hkuros | 309251 | - |
dc.identifier.spage | 2259 | - |
dc.identifier.epage | 2264 | - |
dc.identifier.isi | WOS:000521238102052 | - |
dc.publisher.place | United States | - |