Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather

Three hybrid deep spatio-temporal models are proposed to accurately predict traffic flow under normal conditions, on holidays and under adverse weather. Each of the proposed models consists of the global and target parts, and fuses the weather and traffic flow data obtained from the target and upstr...

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Autores principales:	Wensong Zhang, Ronghan Yao, Xiaojing Du, Jinsong Ye
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Traffic flow prediction holidays adverse weather hybrid deep spatio-temporal model convolutional long short-term memory (ConvLSTM) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/de72da522d1c45ee95aba4884296acc4
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spelling	oai:doaj.org-article:de72da522d1c45ee95aba4884296acc42021-12-02T00:00:15ZHybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather2169-353610.1109/ACCESS.2021.3127584https://doaj.org/article/de72da522d1c45ee95aba4884296acc42021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9612205/https://doaj.org/toc/2169-3536Three hybrid deep spatio-temporal models are proposed to accurately predict traffic flow under normal conditions, on holidays and under adverse weather. Each of the proposed models consists of the global and target parts, and fuses the weather and traffic flow data obtained from the target and upstream sections. The convolutional neural network (CNN), and the gated recurrent unit (GRU) and convolutional long short-term memory (ConvLSTM) neural networks are selected to analyze the spatio-temporal characteristics of traffic flow data. Then, the three proposed models are verified using three actual cases, including traffic flow prediction under normal conditions, on holidays and under adverse weather. Moreover, the characteristics of traffic flow data on the Independence Day and Thanksgiving Day are discussed, as do the patterns of traffic flow data under heavy rain and strong wind. The experimental results show that: the three new models usually perform better than the existing models under all the situations; different holidays and different types of adverse weather have various impacts on the characteristics of traffic volume and speed data.Wensong ZhangRonghan YaoXiaojing DuJinsong YeIEEEarticleTraffic flow predictionholidaysadverse weatherhybrid deep spatio-temporal modelconvolutional long short-term memory (ConvLSTM)Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157165-157181 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Traffic flow prediction holidays adverse weather hybrid deep spatio-temporal model convolutional long short-term memory (ConvLSTM) Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Traffic flow prediction holidays adverse weather hybrid deep spatio-temporal model convolutional long short-term memory (ConvLSTM) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Wensong Zhang Ronghan Yao Xiaojing Du Jinsong Ye Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather
description	Three hybrid deep spatio-temporal models are proposed to accurately predict traffic flow under normal conditions, on holidays and under adverse weather. Each of the proposed models consists of the global and target parts, and fuses the weather and traffic flow data obtained from the target and upstream sections. The convolutional neural network (CNN), and the gated recurrent unit (GRU) and convolutional long short-term memory (ConvLSTM) neural networks are selected to analyze the spatio-temporal characteristics of traffic flow data. Then, the three proposed models are verified using three actual cases, including traffic flow prediction under normal conditions, on holidays and under adverse weather. Moreover, the characteristics of traffic flow data on the Independence Day and Thanksgiving Day are discussed, as do the patterns of traffic flow data under heavy rain and strong wind. The experimental results show that: the three new models usually perform better than the existing models under all the situations; different holidays and different types of adverse weather have various impacts on the characteristics of traffic volume and speed data.
format	article
author	Wensong Zhang Ronghan Yao Xiaojing Du Jinsong Ye
author_facet	Wensong Zhang Ronghan Yao Xiaojing Du Jinsong Ye
author_sort	Wensong Zhang
title	Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather
title_short	Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather
title_full	Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather
title_fullStr	Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather
title_full_unstemmed	Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather
title_sort	hybrid deep spatio-temporal models for traffic flow prediction on holidays and under adverse weather
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/de72da522d1c45ee95aba4884296acc4
work_keys_str_mv	AT wensongzhang hybriddeepspatiotemporalmodelsfortrafficflowpredictiononholidaysandunderadverseweather AT ronghanyao hybriddeepspatiotemporalmodelsfortrafficflowpredictiononholidaysandunderadverseweather AT xiaojingdu hybriddeepspatiotemporalmodelsfortrafficflowpredictiononholidaysandunderadverseweather AT jinsongye hybriddeepspatiotemporalmodelsfortrafficflowpredictiononholidaysandunderadverseweather
_version_	1718404016959913984

Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather

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