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Improving alighting stop inference accuracy in the trip chaining method using neural networks

Author

Listed:
  • Behrang Assemi

    (Queensland University of Technology (QUT))

  • Azalden Alsger

    (University of Queensland)

  • Mahboobeh Moghaddam

    (University of Queensland)

  • Mark Hickman

    (University of Queensland)

  • Mahmoud Mesbah

    (Amirkabir University of Technology)

Abstract

Public transport origin–destination (OD) estimation based on smartcard data has increasingly been used for transit network planning, passengers’ behaviour analyses and network demand forecasting. While various OD estimation methods using the trip-chaining approach have been developed in recent years, the validity of these estimation methods has not extensively been investigated. This study examines the errors in OD estimation caused by inaccurate inference of alighting stops, to improve the accuracy of the existing trip-chaining algorithms. The distribution of errors is evaluated both at the stop level and the public transport zonal level, given the geographical attributes of each zone and the attributes of the smartcard transactions. While the results show significant associations between zone attributes as well as transaction attributes and the alighting stop inference errors, they undermine the existing algorithm’s assumption that ‘travellers alight a public transport service at a stop which is the closest to their next boarding stop’. Accordingly, this study proposes and evaluates the application of a probabilistic approach using neural networks to infer alighting stops based on a combination of transactional and public transit network attributes. The proposed method is validated using 138,122 smartcard transactions obtained during a normal day in Southeast Queensland, Australia. The results show that our method can improve the accuracy of the existing algorithm by inferring the exact location of 79.5% of the alighting stops and reducing the mean alighting estimation error from 1689 to 503 m for incorrectly estimated alighting stops. At the zonal level, the proposed method also improves the accuracy of the existing algorithm by more than 5%. Finally, the study provides both researchers and practitioners with a method to improve the accuracy of the trip-chaining algorithm and OD estimation, and presents a list of practical guidelines for more effective planning and operation of public transit services.

Suggested Citation

  • Behrang Assemi & Azalden Alsger & Mahboobeh Moghaddam & Mark Hickman & Mahmoud Mesbah, 2020. "Improving alighting stop inference accuracy in the trip chaining method using neural networks," Public Transport, Springer, vol. 12(1), pages 89-121, March.
    • Handle: RePEc:spr:pubtra:v:12:y:2020:i:1:d:10.1007_s12469-019-00218-9
    DOI: 10.1007/s12469-019-00218-9
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    References listed on IDEAS

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    1. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
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    Cited by:

    1. Ziqin Lan & Zixuan Zhang & Jiatao Chen & Ming Cai, 2024. "Inferring alighting bus stops from smart card data combined with cellular signaling data," Transportation, Springer, vol. 51(4), pages 1433-1465, August.
    2. Zhanhong Cheng & Martin Trépanier & Lijun Sun, 2021. "Probabilistic model for destination inference and travel pattern mining from smart card data," Transportation, Springer, vol. 48(4), pages 2035-2053, August.
    3. Jin, Meihan & Wang, Menghan & Gong, Yongxi & Liu, Yu, 2022. "Spatio-temporally constrained origin–destination inferring using public transit fare card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).

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    More about this item

    Keywords

    Origin–destination (OD) estimation; Alighting stop inference; Trip-chaining method; Error distribution; Neural network; Deep learning; Public transport; Smartcard data;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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