Simulating micro-level attributes of railway passengers using big data
In the absence of a comprehensive, representative, and attribute-rich population, a spatial microsimulation is necessary to simulate or reconstruct a population for use in the analysis of complex mobility on the railways. Novel consumer datasets called ‘big-data’ are exhaustive but they only reveal...
Main Authors: | , |
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Format: | Article |
Language: | English |
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Elsevier
2022-12-01
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Series: | Journal of Urban Mobility |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2667091722000152 |
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author | Eusebio Odiari Mark Birkin |
author_facet | Eusebio Odiari Mark Birkin |
author_sort | Eusebio Odiari |
collection | DOAJ |
description | In the absence of a comprehensive, representative, and attribute-rich population, a spatial microsimulation is necessary to simulate or reconstruct a population for use in the analysis of complex mobility on the railways. Novel consumer datasets called ‘big-data’ are exhaustive but they only reveal a subset of the wider population who consume a specific digital service. Further, big-data are measured for a particular purpose and so do not have the broad spectrum of attributes required for their wider application. Harnessing big-data by spatial microsimulation has the potential to resolve the above shortcomings. This paper explores the relative merits of different spatial microsimulation methodologies, and a case study illustrates how best to simulate a micro-population linking rail ticketing big-data with the 2011 Census commute to work data and a National Rail Travel Survey (NRTS). The result is a representative attribute-rich micro-level population, which is likely to have a significant impact on the quality of inputs to strategic, tactical and operational rail-sector analysis planning models. |
first_indexed | 2024-04-11T13:59:00Z |
format | Article |
id | doaj.art-2add35bf7c3040e0af53381e15e361bc |
institution | Directory Open Access Journal |
issn | 2667-0917 |
language | English |
last_indexed | 2024-04-11T13:59:00Z |
publishDate | 2022-12-01 |
publisher | Elsevier |
record_format | Article |
series | Journal of Urban Mobility |
spelling | doaj.art-2add35bf7c3040e0af53381e15e361bc2022-12-22T04:20:11ZengElsevierJournal of Urban Mobility2667-09172022-12-012100027Simulating micro-level attributes of railway passengers using big dataEusebio Odiari0Mark Birkin1Consumer Data Research Centre, Leeds Institute for Data Analytics, LS2 9JT, United Kingdom; School of Geography, University of Leeds, Leeds LS2 9JT, United Kingdom; Corresponding author.Consumer Data Research Centre, Leeds Institute for Data Analytics, LS2 9JT, United KingdomIn the absence of a comprehensive, representative, and attribute-rich population, a spatial microsimulation is necessary to simulate or reconstruct a population for use in the analysis of complex mobility on the railways. Novel consumer datasets called ‘big-data’ are exhaustive but they only reveal a subset of the wider population who consume a specific digital service. Further, big-data are measured for a particular purpose and so do not have the broad spectrum of attributes required for their wider application. Harnessing big-data by spatial microsimulation has the potential to resolve the above shortcomings. This paper explores the relative merits of different spatial microsimulation methodologies, and a case study illustrates how best to simulate a micro-population linking rail ticketing big-data with the 2011 Census commute to work data and a National Rail Travel Survey (NRTS). The result is a representative attribute-rich micro-level population, which is likely to have a significant impact on the quality of inputs to strategic, tactical and operational rail-sector analysis planning models.http://www.sciencedirect.com/science/article/pii/S2667091722000152Spatial microsimulationPopulation synthesisMicro-level attributesRailwaysBig dataConsumer data |
spellingShingle | Eusebio Odiari Mark Birkin Simulating micro-level attributes of railway passengers using big data Journal of Urban Mobility Spatial microsimulation Population synthesis Micro-level attributes Railways Big data Consumer data |
title | Simulating micro-level attributes of railway passengers using big data |
title_full | Simulating micro-level attributes of railway passengers using big data |
title_fullStr | Simulating micro-level attributes of railway passengers using big data |
title_full_unstemmed | Simulating micro-level attributes of railway passengers using big data |
title_short | Simulating micro-level attributes of railway passengers using big data |
title_sort | simulating micro level attributes of railway passengers using big data |
topic | Spatial microsimulation Population synthesis Micro-level attributes Railways Big data Consumer data |
url | http://www.sciencedirect.com/science/article/pii/S2667091722000152 |
work_keys_str_mv | AT eusebioodiari simulatingmicrolevelattributesofrailwaypassengersusingbigdata AT markbirkin simulatingmicrolevelattributesofrailwaypassengersusingbigdata |