Research on origin‐destination travel demand prediction method of inter‐regional online taxi based on SpatialOD‐BiConvLSTM

Abstract In order to avoid empty or crowded trips of online taxis and shorten the distance between empty taxis and potential passengers, after adopting the Origin‐Destination (OD) big data of taxis and considering geographic location information, cross‐regional driving demand, as well as passenger o...

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Autores principales:	Dejun Chen, Jing Wang, Congcong Xiong
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Transportation engineering TA1001-1280 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/391fd74e126f46e59629b21f20c32887
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spelling	oai:doaj.org-article:391fd74e126f46e59629b21f20c328872021-11-11T10:16:46ZResearch on origin‐destination travel demand prediction method of inter‐regional online taxi based on SpatialOD‐BiConvLSTM1751-95781751-956X10.1049/itr2.12119https://doaj.org/article/391fd74e126f46e59629b21f20c328872021-12-01T00:00:00Zhttps://doi.org/10.1049/itr2.12119https://doaj.org/toc/1751-956Xhttps://doaj.org/toc/1751-9578Abstract In order to avoid empty or crowded trips of online taxis and shorten the distance between empty taxis and potential passengers, after adopting the Origin‐Destination (OD) big data of taxis and considering geographic location information, cross‐regional driving demand, as well as passenger origin demand and destination demand, a method combining SpatialOD and Bidirectional ConvLSTM (BiConvLSTM) neural network model is proposed to predict online taxi travel demand. The network model in the method takes into account the historical and future states of the data, and can extract the time and space characteristics of the data. A custom structural similarity loss function is used in the model training to measure the difference between the data. Through experiments on a large public taxi travel data set, the results show that the SMAPE error of this method is 23.83%, which is better than the existing OD demand forecasting methods and has strong practicability.Dejun ChenJing WangCongcong XiongWileyarticleTransportation engineeringTA1001-1280Electronic computers. Computer scienceQA75.5-76.95ENIET Intelligent Transport Systems, Vol 15, Iss 12, Pp 1533-1547 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Transportation engineering TA1001-1280 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Transportation engineering TA1001-1280 Electronic computers. Computer science QA75.5-76.95 Dejun Chen Jing Wang Congcong Xiong Research on origin‐destination travel demand prediction method of inter‐regional online taxi based on SpatialOD‐BiConvLSTM
description	Abstract In order to avoid empty or crowded trips of online taxis and shorten the distance between empty taxis and potential passengers, after adopting the Origin‐Destination (OD) big data of taxis and considering geographic location information, cross‐regional driving demand, as well as passenger origin demand and destination demand, a method combining SpatialOD and Bidirectional ConvLSTM (BiConvLSTM) neural network model is proposed to predict online taxi travel demand. The network model in the method takes into account the historical and future states of the data, and can extract the time and space characteristics of the data. A custom structural similarity loss function is used in the model training to measure the difference between the data. Through experiments on a large public taxi travel data set, the results show that the SMAPE error of this method is 23.83%, which is better than the existing OD demand forecasting methods and has strong practicability.
format	article
author	Dejun Chen Jing Wang Congcong Xiong
author_facet	Dejun Chen Jing Wang Congcong Xiong
author_sort	Dejun Chen
title	Research on origin‐destination travel demand prediction method of inter‐regional online taxi based on SpatialOD‐BiConvLSTM
title_short	Research on origin‐destination travel demand prediction method of inter‐regional online taxi based on SpatialOD‐BiConvLSTM
title_full	Research on origin‐destination travel demand prediction method of inter‐regional online taxi based on SpatialOD‐BiConvLSTM
title_fullStr	Research on origin‐destination travel demand prediction method of inter‐regional online taxi based on SpatialOD‐BiConvLSTM
title_full_unstemmed	Research on origin‐destination travel demand prediction method of inter‐regional online taxi based on SpatialOD‐BiConvLSTM
title_sort	research on origin‐destination travel demand prediction method of inter‐regional online taxi based on spatialod‐biconvlstm
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/391fd74e126f46e59629b21f20c32887
work_keys_str_mv	AT dejunchen researchonorigindestinationtraveldemandpredictionmethodofinterregionalonlinetaxibasedonspatialodbiconvlstm AT jingwang researchonorigindestinationtraveldemandpredictionmethodofinterregionalonlinetaxibasedonspatialodbiconvlstm AT congcongxiong researchonorigindestinationtraveldemandpredictionmethodofinterregionalonlinetaxibasedonspatialodbiconvlstm
_version_	1718439206137626624

Research on origin‐destination travel demand prediction method of inter‐regional online taxi based on SpatialOD‐BiConvLSTM

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