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: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
|
| Series: | Journal of Urban Mobility |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667091722000152 |
| _version_ | 1811187177725362176 |
|---|---|
| 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 |