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A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels

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  • Lovelace, Robin
  • Ballas, Dimitris
  • Watson, Matt

Abstract

The daily trip to work is ubiquitous, yet its characteristics differ widely from person to person and place to place. This is manifested in statistics on mode and distance of travel, which vary depending on a range of factors that operate at different scales. This heterogeneity is problematic for decision makers tasked with encouraging more sustainable commuter patterns. Numerical models, based on real commuting data, have great potential to aid the decision making process. However, we contend that new approaches are needed to advance knowledge about the social and geographical factors that relate to the diversity of commuter patterns, if policies targeted to specific individuals or places are to be effective. To this end, the paper presents a spatial microsimulation approach, which combines individual-level survey data with geographically aggregated census results to tackle the problem. This method overcomes the limitations imposed by the lack of available geocoded micro-data. Further, it allows a range of scales of analysis to be pursued in parallel and provides insights into both the types of area and individual that would benefit most from specific interventions.

Suggested Citation

  • Lovelace, Robin & Ballas, Dimitris & Watson, Matt, 2014. "A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels," Journal of Transport Geography, Elsevier, vol. 34(C), pages 282-296.
    • Handle: RePEc:eee:jotrge:v:34:y:2014:i:c:p:282-296
    DOI: 10.1016/j.jtrangeo.2013.07.008
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    3. Dewulf, Bart & Neutens, Tijs & Vanlommel, Mario & Logghe, Steven & De Maeyer, Philippe & Witlox, Frank & De Weerdt, Yves & Van de Weghe, Nico, 2015. "Examining commuting patterns using Floating Car Data and circular statistics: Exploring the use of new methods and visualizations to study travel times," Journal of Transport Geography, Elsevier, vol. 48(C), pages 41-51.
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    5. Kuehnel, Nico & Ziemke, Dominik & Moeckel, Rolf & Nagel, Kai, 2020. "The end of travel time matrices: Individual travel times in integrated land use/transport models," Journal of Transport Geography, Elsevier, vol. 88(C).
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    7. Jiri Horak & Jan Tesla & David Fojtik & Vit Vozenilek, 2019. "Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava," Sustainability, MDPI, vol. 11(24), pages 1-25, December.
    8. Hainan Huang & Yi Lin & Jiancheng Weng & Jian Rong & Xiaoming Liu, 2018. "Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway," Sustainability, MDPI, vol. 10(12), pages 1-15, December.
    9. Ma, Xiaolei & Liu, Congcong & Wen, Huimin & Wang, Yunpeng & Wu, Yao-Jan, 2017. "Understanding commuting patterns using transit smart card data," Journal of Transport Geography, Elsevier, vol. 58(C), pages 135-145.
    10. Stuart Donovan & Thomas de Graaff & Henri L.F. de Groot, 2023. "An inexact science: Accounting for measurement error and downward bias in mode and location choice models," Tinbergen Institute Discussion Papers 23-010/VIII, Tinbergen Institute.
    11. Ruihong Huang, 2019. "Simulating individual work trips for transit-facilitated accessibility study," Environment and Planning B, , vol. 46(1), pages 84-102, January.
    12. Hadrien Salat & Dustin Carlino & Fernando Benitez-Paez & Anna Zanchetta & Daniel Arribas-Bel & Mark Birkin, 2023. "Synthetic population Catalyst: A micro-simulated population of England with circadian activities," Environment and Planning B, , vol. 50(8), pages 2309-2316, October.
    13. Dianna M. Smith & Alison Heppenstall & Monique Campbell, 2021. "Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health," J, MDPI, vol. 4(2), pages 1-11, June.
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    15. Eveline van Leeuwen & Yoshifumi Ishikawa & Peter Nijkamp, 2016. "Microsimulation and interregional input–output modelling as tools for multi-level policy analysis," Environment and Planning C, , vol. 34(1), pages 135-150, February.

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