Estimating the residence zone of frequent public transport users to make travel pattern and time use analysis

Public transport systems with electronic fare collection devices continuously store data related to trips taken by users, which contain valuable information for planning and policy analysis. However, if the card is not personalized, there is no socioeconomic information available, which imposes a limitation on the types of analysis that can be performed. This work presents a simple method to estimate the residence zone of card users, which will allow socioeconomic variables to be estimated, thereby enriching the analytical possibilities. The method, which is based on the observation of morning transactions of frequent users, is applied to a sample of over 2 million cards. The method is evaluated using a sample from the Santiago ODS where users declared their card id and also declared their home address. A sample of 888,970 cards that are observed at least three days in a week and show spatial regularity for the morning transaction is used for zone of residence estimation and analysis of travel patterns and time use. The results show that users who live in the city center or in the wealthier East zone experience lower travel time, spend more time at home and less time at work.

[1]  Peter White,et al.  The Potential of Public Transport Smart Card Data , 2005 .

[2]  Xiaolei Ma,et al.  Mining smart card data for transit riders’ travel patterns , 2013 .

[3]  Yasuo Asakura,et al.  Behavioural data mining of transit smart card data: A data fusion approach , 2014 .

[4]  Marcela Munizaga,et al.  Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile , 2012 .

[5]  Catherine Morency,et al.  Smart card data use in public transit: A literature review , 2011 .

[6]  Yunpeng Wang,et al.  Understanding commuting patterns using transit smart card data , 2017 .

[7]  Bruno Agard,et al.  Measuring transit use variability with smart-card data , 2007 .

[8]  Haris N. Koutsopoulos,et al.  Inferring patterns in the multi-week activity sequences of public transport users , 2016 .

[9]  Marcela Munizaga,et al.  Validating travel behavior estimated from smartcard data , 2013 .

[10]  Zhenliang Ma,et al.  Activity detection and transfer identification for public transit fare card data , 2015 .

[11]  Sang Gu Lee,et al.  Travel Pattern Analysis Using Smart Card Data of Regular Users , 2011 .

[12]  Antonio Gschwender,et al.  Using smart card and GPS data for policy and planning: The case of Transantiago , 2016 .

[13]  Viviana Muñoz,et al.  Encuesta origen-destino de Santiago 2012: Resultados y validaciones , 2016 .

[14]  Martin Trépanier,et al.  Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System , 2007, J. Intell. Transp. Syst..

[15]  Sergio R. Jara-Díaz,et al.  Understanding time use: Daily or weekly data? , 2015 .

[16]  S. Jara-Díaz,et al.  The role of gender, age and location in the values of work behind time use patterns in Santiago, Chile , 2011 .

[17]  M. Trépanier,et al.  Detection of Activities of Public Transport Users by Analyzing Smart Card Data , 2012 .

[18]  Análisis de Patrones de Actividades a partir de la EOD 2001 , 2009 .

[19]  Nigel H. M. Wilson,et al.  Potential Uses of Transit Smart Card Registration and Transaction Data to Improve Transit Planning , 2006 .

[20]  Dong-Jun Kim,et al.  Use of Smart Card Data to Define Public Transit Use in Seoul, South Korea , 2008 .