Multi-Scale Non-Local Spatio-Temporal Information Fusion Networks for Multi-Step Traffic Flow Forecasting
Traffic flow prediction is a crucial research area in traffic management. Accurately predicting traffic flow in each area of the city over the long term can enable city managers to make informed decisions regarding the allocation of urban transportation resources in the future. The existing traffic...
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Format: | Article |
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MDPI AG
2024-02-01
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Series: | ISPRS International Journal of Geo-Information |
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Online Access: | https://www.mdpi.com/2220-9964/13/3/71 |
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author | Shuai Lu Haibo Chen Yilong Teng |
author_facet | Shuai Lu Haibo Chen Yilong Teng |
author_sort | Shuai Lu |
collection | DOAJ |
description | Traffic flow prediction is a crucial research area in traffic management. Accurately predicting traffic flow in each area of the city over the long term can enable city managers to make informed decisions regarding the allocation of urban transportation resources in the future. The existing traffic flow prediction models either give insufficient attention to the interactions of long-lasting spatio-temporal regions or extract spatio-temporal features in a single scale, which ignores the identification of traffic flow patterns at various scales. In this paper, we present a multi-scale spatio-temporal information fusion model using non-local networks, which fuses traffic flow pattern features at multiple scales in space and time, complemented by non-local networks to construct the global direct dependence relationship between local areas and the entire region of the city in space and time in the past. The proposed model is evaluated through experiments and is shown to outperform existing benchmark models in terms of prediction performance. |
first_indexed | 2024-04-24T18:12:20Z |
format | Article |
id | doaj.art-5bddc668fe07418a927cdcaf66e623cb |
institution | Directory Open Access Journal |
issn | 2220-9964 |
language | English |
last_indexed | 2024-04-24T18:12:20Z |
publishDate | 2024-02-01 |
publisher | MDPI AG |
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series | ISPRS International Journal of Geo-Information |
spelling | doaj.art-5bddc668fe07418a927cdcaf66e623cb2024-03-27T13:44:51ZengMDPI AGISPRS International Journal of Geo-Information2220-99642024-02-011337110.3390/ijgi13030071Multi-Scale Non-Local Spatio-Temporal Information Fusion Networks for Multi-Step Traffic Flow ForecastingShuai Lu0Haibo Chen1Yilong Teng2School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaTraffic flow prediction is a crucial research area in traffic management. Accurately predicting traffic flow in each area of the city over the long term can enable city managers to make informed decisions regarding the allocation of urban transportation resources in the future. The existing traffic flow prediction models either give insufficient attention to the interactions of long-lasting spatio-temporal regions or extract spatio-temporal features in a single scale, which ignores the identification of traffic flow patterns at various scales. In this paper, we present a multi-scale spatio-temporal information fusion model using non-local networks, which fuses traffic flow pattern features at multiple scales in space and time, complemented by non-local networks to construct the global direct dependence relationship between local areas and the entire region of the city in space and time in the past. The proposed model is evaluated through experiments and is shown to outperform existing benchmark models in terms of prediction performance.https://www.mdpi.com/2220-9964/13/3/71traffic flow predictionmulti-scale spatio-temporal fusionspatio-temporal regional correlationconvolutional LSTMnon-local network |
spellingShingle | Shuai Lu Haibo Chen Yilong Teng Multi-Scale Non-Local Spatio-Temporal Information Fusion Networks for Multi-Step Traffic Flow Forecasting ISPRS International Journal of Geo-Information traffic flow prediction multi-scale spatio-temporal fusion spatio-temporal regional correlation convolutional LSTM non-local network |
title | Multi-Scale Non-Local Spatio-Temporal Information Fusion Networks for Multi-Step Traffic Flow Forecasting |
title_full | Multi-Scale Non-Local Spatio-Temporal Information Fusion Networks for Multi-Step Traffic Flow Forecasting |
title_fullStr | Multi-Scale Non-Local Spatio-Temporal Information Fusion Networks for Multi-Step Traffic Flow Forecasting |
title_full_unstemmed | Multi-Scale Non-Local Spatio-Temporal Information Fusion Networks for Multi-Step Traffic Flow Forecasting |
title_short | Multi-Scale Non-Local Spatio-Temporal Information Fusion Networks for Multi-Step Traffic Flow Forecasting |
title_sort | multi scale non local spatio temporal information fusion networks for multi step traffic flow forecasting |
topic | traffic flow prediction multi-scale spatio-temporal fusion spatio-temporal regional correlation convolutional LSTM non-local network |
url | https://www.mdpi.com/2220-9964/13/3/71 |
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