Resumé

The emergence of new tracking technologies and Big Data has caused a transformation of the transport geography field in recent years. One new datatype, which is starting to play a significant role in public transport, is smart card data. Despite the growing focus on smart card data, there is a need for studies that explicitly compare the quality of this new type of data to traditional data sources.

With the current focus on Big Data in the transport field, public transport planners are increasingly looking towards smart card data to analyze and optimize flows of passengers. However, in many cases it is not all public transport passengers in a city, region or country with a smart card system that uses the system, and in such cases, it is important to know what biases smart card data has in relation to giving a complete view upon passenger flows.

This paper therefore analyses the quality and biases of smart card data in Denmark, where public transport passengers may use a smart card, may pay with cash for individual trips or may hold a season ticket for a certain route. By analyzing smart card data collected in Denmark in relation to data on sales of cash tickets, sales of season tickets, manual annual passenger counts and continuous automated door counts in busses and trains, and travel survey data, this article estimates quality and biases of using smart card data to illuminate the complete flow of public transport passengers in Denmark.
OriginalsprogEngelsk
Publikationsdato2016
Antal sider1
StatusUdgivet - 2016
BegivenhedAssociation of American Geographers Annual Meeting 2016 - San Francisco, USA
Varighed: 29 mar. 20162 apr. 2016
http://www.aag.org/annualmeeting

Konference

KonferenceAssociation of American Geographers Annual Meeting 2016
LandUSA
BySan Francisco
Periode29/03/201602/04/2016
Internetadresse

Fingerprint

Smart cards
Sales
Big data

Emneord

  • Transport geography
  • Big data
  • Smart card
  • Tracking
  • Public transport

Citer dette

Reinau, K. H., Agerholm, N., & Lahrmann, H. S. (2016). Big Data in Transport Geography: Estimating the quality of Smart Card Data. Abstract fra Association of American Geographers Annual Meeting 2016, San Francisco, USA.
Reinau, Kristian Hegner ; Agerholm, Niels ; Lahrmann, Harry Spaabæk. / Big Data in Transport Geography : Estimating the quality of Smart Card Data. Abstract fra Association of American Geographers Annual Meeting 2016, San Francisco, USA.1 s.
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Reinau, KH, Agerholm, N & Lahrmann, HS 2016, 'Big Data in Transport Geography: Estimating the quality of Smart Card Data' Association of American Geographers Annual Meeting 2016, San Francisco, USA, 29/03/2016 - 02/04/2016, .

Big Data in Transport Geography : Estimating the quality of Smart Card Data. / Reinau, Kristian Hegner; Agerholm, Niels; Lahrmann, Harry Spaabæk.

2016. Abstract fra Association of American Geographers Annual Meeting 2016, San Francisco, USA.

Publikation: Konferencebidrag uden forlag/tidsskriftKonferenceabstrakt til konferenceForskning

TY - ABST

T1 - Big Data in Transport Geography

T2 - Estimating the quality of Smart Card Data

AU - Reinau, Kristian Hegner

AU - Agerholm, Niels

AU - Lahrmann, Harry Spaabæk

PY - 2016

Y1 - 2016

N2 - The emergence of new tracking technologies and Big Data has caused a transformation of the transport geography field in recent years. One new datatype, which is starting to play a significant role in public transport, is smart card data. Despite the growing focus on smart card data, there is a need for studies that explicitly compare the quality of this new type of data to traditional data sources.With the current focus on Big Data in the transport field, public transport planners are increasingly looking towards smart card data to analyze and optimize flows of passengers. However, in many cases it is not all public transport passengers in a city, region or country with a smart card system that uses the system, and in such cases, it is important to know what biases smart card data has in relation to giving a complete view upon passenger flows. This paper therefore analyses the quality and biases of smart card data in Denmark, where public transport passengers may use a smart card, may pay with cash for individual trips or may hold a season ticket for a certain route. By analyzing smart card data collected in Denmark in relation to data on sales of cash tickets, sales of season tickets, manual annual passenger counts and continuous automated door counts in busses and trains, and travel survey data, this article estimates quality and biases of using smart card data to illuminate the complete flow of public transport passengers in Denmark.

AB - The emergence of new tracking technologies and Big Data has caused a transformation of the transport geography field in recent years. One new datatype, which is starting to play a significant role in public transport, is smart card data. Despite the growing focus on smart card data, there is a need for studies that explicitly compare the quality of this new type of data to traditional data sources.With the current focus on Big Data in the transport field, public transport planners are increasingly looking towards smart card data to analyze and optimize flows of passengers. However, in many cases it is not all public transport passengers in a city, region or country with a smart card system that uses the system, and in such cases, it is important to know what biases smart card data has in relation to giving a complete view upon passenger flows. This paper therefore analyses the quality and biases of smart card data in Denmark, where public transport passengers may use a smart card, may pay with cash for individual trips or may hold a season ticket for a certain route. By analyzing smart card data collected in Denmark in relation to data on sales of cash tickets, sales of season tickets, manual annual passenger counts and continuous automated door counts in busses and trains, and travel survey data, this article estimates quality and biases of using smart card data to illuminate the complete flow of public transport passengers in Denmark.

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Reinau KH, Agerholm N, Lahrmann HS. Big Data in Transport Geography: Estimating the quality of Smart Card Data. 2016. Abstract fra Association of American Geographers Annual Meeting 2016, San Francisco, USA.