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- Publisher Website: 10.1109/TITS.2019.2924971
- Scopus: eid_2-s2.0-85089890706
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Article: Deep Multi-Scale Convolutional LSTM Network for Travel Demand and Origin-Destination Predictions
Title | Deep Multi-Scale Convolutional LSTM Network for Travel Demand and Origin-Destination Predictions |
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Authors | |
Keywords | Deep learning Public transportation Correlation Predictive models Data models |
Issue Date | 2020 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979 |
Citation | IEEE Transactions on Intelligent Transportation Systems, 2020, v. 21 n. 8, p. 3219-3232 How to Cite? |
Abstract | Advancements in sensing and the Internet of Things (IoT) technologies generate a huge amount of data. Mobility on demand (MoD) service benefits from the availability of big data in the intelligent transportation system. Given the future travel demand or origin-destination (OD) flows prediction, service providers can pre-allocate unoccupied vehicles to the customers' origins of service to reduce waiting time. Traditional approaches on future travel demand and the OD flows predictions rely on statistical or machine learning methods. Inspired by deep learning techniques for image and video processing, through regarding localized travel demands as image pixels, a novel deep learning model called multi-scale convolutional long short-term memory network (MultiConvLSTM) is developed in this paper. Rather than using the traditional OD matrix which may lead to loss of geographical information, we propose a new data structure, called OD tensor to represent OD flows, and a manipulation method, called OD tensor permutation and matricization, is introduced to handle the high dimensionality features of OD tensor. MultiConvLSTM considers both temporal and spatial correlations to predict the future travel demand and OD flows. Experiments on real-world New York taxi data of around 400 million records are performed. Our results show that the MultiConvLSTM achieves the highest accuracy in both one-step and multiple-step predictions and it outperforms the existing methods for travel demand and OD flow predictions. |
Persistent Identifier | http://hdl.handle.net/10722/287935 |
ISSN | 2023 Impact Factor: 7.9 2023 SCImago Journal Rankings: 2.580 |
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 | Li, VOK | - |
dc.date.accessioned | 2020-10-05T12:05:24Z | - |
dc.date.available | 2020-10-05T12:05:24Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Intelligent Transportation Systems, 2020, v. 21 n. 8, p. 3219-3232 | - |
dc.identifier.issn | 1524-9050 | - |
dc.identifier.uri | http://hdl.handle.net/10722/287935 | - |
dc.description.abstract | Advancements in sensing and the Internet of Things (IoT) technologies generate a huge amount of data. Mobility on demand (MoD) service benefits from the availability of big data in the intelligent transportation system. Given the future travel demand or origin-destination (OD) flows prediction, service providers can pre-allocate unoccupied vehicles to the customers' origins of service to reduce waiting time. Traditional approaches on future travel demand and the OD flows predictions rely on statistical or machine learning methods. Inspired by deep learning techniques for image and video processing, through regarding localized travel demands as image pixels, a novel deep learning model called multi-scale convolutional long short-term memory network (MultiConvLSTM) is developed in this paper. Rather than using the traditional OD matrix which may lead to loss of geographical information, we propose a new data structure, called OD tensor to represent OD flows, and a manipulation method, called OD tensor permutation and matricization, is introduced to handle the high dimensionality features of OD tensor. MultiConvLSTM considers both temporal and spatial correlations to predict the future travel demand and OD flows. Experiments on real-world New York taxi data of around 400 million records are performed. Our results show that the MultiConvLSTM achieves the highest accuracy in both one-step and multiple-step predictions and it outperforms the existing methods for travel demand and OD flow predictions. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979 | - |
dc.relation.ispartof | IEEE Transactions on Intelligent Transportation Systems | - |
dc.rights | IEEE Transactions on Intelligent Transportation Systems. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx 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.subject | Deep learning | - |
dc.subject | Public transportation | - |
dc.subject | Correlation | - |
dc.subject | Predictive models | - |
dc.subject | Data models | - |
dc.title | Deep Multi-Scale Convolutional LSTM Network for Travel Demand and Origin-Destination Predictions | - |
dc.type | Article | - |
dc.identifier.email | Lam, AYS: ayslam@eee.hku.hk | - |
dc.identifier.email | Li, VOK: vli@eee.hku.hk | - |
dc.identifier.authority | Lam, AYS=rp02083 | - |
dc.identifier.authority | Li, VOK=rp00150 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TITS.2019.2924971 | - |
dc.identifier.scopus | eid_2-s2.0-85089890706 | - |
dc.identifier.hkuros | 315117 | - |
dc.identifier.volume | 21 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | 3219 | - |
dc.identifier.epage | 3232 | - |
dc.identifier.isi | WOS:000554907200007 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1524-9050 | - |