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Before-and-after evaluation of a bus network improvement using performance indicators from historical smart card data

Author

Listed:
  • Renato Arbex

    (Escola Politécnica da USP, Universidade de São Paulo)

  • Claudio Barbieri Cunha

    (Escola Politécnica da USP, Universidade de São Paulo)

  • Roberto Speicys

    (Scipopulis Desenvolvimento e Análise de Dados Ltda)

Abstract

This paper describes a methodology for assessing before-and-after benefits for residents in an urban area resulting from the implementation of a major bus network improvement. The new late-night bus network of the city of São Paulo, Brazil is used as a case study. A group of supply and demand performance indicators is proposed in order to evaluate changes on all network levels. Accessibility analyses are conducted to evaluate how changes in the supply side potentially impact easiness of reaching destinations, while an analysis of the demand side response is performed by comparing changes in origin and destination transit trip volumes in a three-year period using historical smart card and GPS data, as well as differences in average travel times and number of transfers per trip within the network.

Suggested Citation

  • Renato Arbex & Claudio Barbieri Cunha & Roberto Speicys, 2021. "Before-and-after evaluation of a bus network improvement using performance indicators from historical smart card data," Public Transport, Springer, vol. 13(3), pages 483-501, October.
    • Handle: RePEc:spr:pubtra:v:13:y:2021:i:3:d:10.1007_s12469-019-00214-z
    DOI: 10.1007/s12469-019-00214-z
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    References listed on IDEAS

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    1. Arbex, Renato Oliveira & da Cunha, Claudio Barbieri, 2015. "Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 355-376.
    2. Birch, Colin P.D. & Oom, Sander P. & Beecham, Jonathan A., 2007. "Rectangular and hexagonal grids used for observation, experiment and simulation in ecology," Ecological Modelling, Elsevier, vol. 206(3), pages 347-359.
    3. Owen, Andrew & Levinson, David M., 2015. "Modeling the commute mode share of transit using continuous accessibility to jobs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 110-122.
    4. Neema Nassir & Mark Hickman & Zhen-Liang Ma, 2015. "Activity detection and transfer identification for public transit fare card data," Transportation, Springer, vol. 42(4), pages 683-705, July.
    5. Michael Iacono & David Levinson, 2017. "Accessibility dynamics and location premia: Do land values follow accessibility changes?," Urban Studies, Urban Studies Journal Limited, vol. 54(2), pages 364-381, February.
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    Cited by:

    1. Zhang, Yingheng & Li, Haojie & Ren, Gang, 2022. "Quantifying the social impacts of the London Night Tube with a double/debiased machine learning based difference-in-differences approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 288-303.

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