Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning

Predicting the travel demand plays an indispensable role in urban transportation planning. Data collection methods for estimating the origin–destination (OD) demand matrix are being extensively shifted from traditional survey techniques to the pre-collected data from intelligent transportation syste...

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Autores principales:	Shahriar Afandizadeh Zargari, Amirmasoud Memarnejad, Hamid Mirzahossein
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hourly OD demand matrix machine learning big data neural network Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/d42147b576eb40188592a7fa824870e8
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spelling	oai:doaj.org-article:d42147b576eb40188592a7fa824870e82021-11-11T19:05:52ZHourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning10.3390/s212170801424-8220https://doaj.org/article/d42147b576eb40188592a7fa824870e82021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7080https://doaj.org/toc/1424-8220Predicting the travel demand plays an indispensable role in urban transportation planning. Data collection methods for estimating the origin–destination (OD) demand matrix are being extensively shifted from traditional survey techniques to the pre-collected data from intelligent transportation systems (ITSs). This shift is partly due to the high cost of conducting traditional surveys and partly due to the diversity of scattered data produced by ITSs and the opportunity to derive extra benefits out of this big data. This study attempts to predict the OD matrix of Tehran metropolis using a set of ITS data, including the data extracted from automatic number plate recognition (ANPR) cameras, smart fare cards, loop detectors at intersections, global positioning systems (GPS) of navigation software, socio-economic and demographic characteristics as well as land-use features of zones. For this purpose, five models based on machine learning (ML) techniques are developed for training and test. In evaluating the performance of the models, the statistical methods show that the convolutional neural network (CNN) leads to the best performance in terms of accuracy in predicting the OD matrix and has the lowest error in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the predicted OD matrix was structurally compared with the ground truth matrix, and the CNN model also shows the highest structural similarity with the ground truth OD matrix in the presented case.Shahriar Afandizadeh ZargariAmirmasoud MemarnejadHamid MirzahosseinMDPI AGarticlehourly OD demand matrixmachine learningbig dataneural networkChemical technologyTP1-1185ENSensors, Vol 21, Iss 7080, p 7080 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hourly OD demand matrix machine learning big data neural network Chemical technology TP1-1185
spellingShingle	hourly OD demand matrix machine learning big data neural network Chemical technology TP1-1185 Shahriar Afandizadeh Zargari Amirmasoud Memarnejad Hamid Mirzahossein Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning
description	Predicting the travel demand plays an indispensable role in urban transportation planning. Data collection methods for estimating the origin–destination (OD) demand matrix are being extensively shifted from traditional survey techniques to the pre-collected data from intelligent transportation systems (ITSs). This shift is partly due to the high cost of conducting traditional surveys and partly due to the diversity of scattered data produced by ITSs and the opportunity to derive extra benefits out of this big data. This study attempts to predict the OD matrix of Tehran metropolis using a set of ITS data, including the data extracted from automatic number plate recognition (ANPR) cameras, smart fare cards, loop detectors at intersections, global positioning systems (GPS) of navigation software, socio-economic and demographic characteristics as well as land-use features of zones. For this purpose, five models based on machine learning (ML) techniques are developed for training and test. In evaluating the performance of the models, the statistical methods show that the convolutional neural network (CNN) leads to the best performance in terms of accuracy in predicting the OD matrix and has the lowest error in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the predicted OD matrix was structurally compared with the ground truth matrix, and the CNN model also shows the highest structural similarity with the ground truth OD matrix in the presented case.
format	article
author	Shahriar Afandizadeh Zargari Amirmasoud Memarnejad Hamid Mirzahossein
author_facet	Shahriar Afandizadeh Zargari Amirmasoud Memarnejad Hamid Mirzahossein
author_sort	Shahriar Afandizadeh Zargari
title	Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning
title_short	Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning
title_full	Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning
title_fullStr	Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning
title_full_unstemmed	Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning
title_sort	hourly origin–destination matrix estimation using intelligent transportation systems data and deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d42147b576eb40188592a7fa824870e8
work_keys_str_mv	AT shahriarafandizadehzargari hourlyorigindestinationmatrixestimationusingintelligenttransportationsystemsdataanddeeplearning AT amirmasoudmemarnejad hourlyorigindestinationmatrixestimationusingintelligenttransportationsystemsdataanddeeplearning AT hamidmirzahossein hourlyorigindestinationmatrixestimationusingintelligenttransportationsystemsdataanddeeplearning
_version_	1718431672759746560

Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning

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