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Analysis of demand–supply gaps in public transit systems based on census and GTFS data: a case study of Calgary, Canada

Author

Listed:
  • Koragot Kaeoruean

    (Chiang Mai University)

  • Santi Phithakkitnukoon

    (Chiang Mai University
    Chiang Mai University)

  • Merkebe Getachew Demissie

    (University of Calgary)

  • Lina Kattan

    (University of Calgary)

  • Carlo Ratti

    (Massachusetts Institute of Technology)

Abstract

Bridging the gap between demand and supply in transit service is crucial for public transportation management, as planning actions can be implemented to generate supply in high demand areas or to improve upon inefficient deployment of transit service in low transit demand areas. This study aims to introduce feasible approaches for measuring gap types 1 and 2. Gap type 1 measures the gap between public transit capacity and the number of public transit riders per area, while gap type 2 measures the gap between demand and supply as a normalized index. Gap type 1 provides a value that is more realistic than gap type 2, but it requires detailed passenger data that is not always readily available. Gap type 2 is a practical alternative when the detailed passenger data is unavailable because it uses a weighting scheme to estimate demand values. It also uses a newly proposed normalization method called M-score, which allows for a longitudinal gap analysis where yearly gap patterns and trends can be observed and compared. A 5-year gap analysis of Calgary transit is used as a case study. This work presents a new perspective of hourly gaps and proposes a gap measurement approach that contributes to public transit system planning and service improvement.

Suggested Citation

  • Koragot Kaeoruean & Santi Phithakkitnukoon & Merkebe Getachew Demissie & Lina Kattan & Carlo Ratti, 2020. "Analysis of demand–supply gaps in public transit systems based on census and GTFS data: a case study of Calgary, Canada," Public Transport, Springer, vol. 12(3), pages 483-516, October.
    • Handle: RePEc:spr:pubtra:v:12:y:2020:i:3:d:10.1007_s12469-020-00252-y
    DOI: 10.1007/s12469-020-00252-y
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    References listed on IDEAS

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    1. Currie, Graham, 2010. "Quantifying spatial gaps in public transport supply based on social needs," Journal of Transport Geography, Elsevier, vol. 18(1), pages 31-41.
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    Cited by:

    1. Yunes Almansoub & Ming Zhong & Muhammad Safdar & Asif Raza & Abdelghani Dahou & Mohammed A. A. Al-qaness, 2023. "Modeling Impact of Transportation Infrastructure-Based Accessibility on the Development of Mixed Land Use Using Deep Neural Networks: Evidence from Jiang’an District, City of Wuhan, China," Sustainability, MDPI, vol. 15(21), pages 1-40, October.
    2. Merkebe Getachew Demissie & Lina Kattan, 2022. "Understanding the temporal and spatial interactions between transit ridership and urban land-use patterns: an exploratory study," Public Transport, Springer, vol. 14(2), pages 385-417, June.
    3. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    4. Mahmood Mahmoodi Nesheli & Siva Srikukenthiran & Amer Shalaby, 2022. "An optimization model for planning limited-stop transit operations," Public Transport, Springer, vol. 14(1), pages 63-83, March.
    5. Marc-Edouard Schultheiss, 2022. "Assessment of the Bus Transit Network: A Perspective from the Daily Activity-Travel Organization of Travelers," Sustainability, MDPI, vol. 14(4), pages 1-20, February.
    6. Juan Godfrid & Pablo Radnic & Alejandro Vaisman & Esteban Zimányi, 2022. "Analyzing public transport in the city of Buenos Aires with MobilityDB," Public Transport, Springer, vol. 14(2), pages 287-321, June.
    7. Zack Aemmer & Andisheh Ranjbari & Don MacKenzie, 2022. "Measurement and classification of transit delays using GTFS-RT data," Public Transport, Springer, vol. 14(2), pages 263-285, June.

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