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

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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.

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Notes

  1. These assumptions should continue evolving, considering the emergence and widespread use of new modes of transport, such as (e-)scooters and light (e-)bikes.

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Acknowledgements

This research was funded by Queensland Department of Transport and Main Roads (TMR), under the TAP agreement with the University of Queensland Centre for Transport Strategy.

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Authors and Affiliations

  1. School of Civil Engineering and Built Environment, Queensland University of Technology (QUT), Brisbane, Australia

    Behrang Assemi

  2. School of Civil Engineering, University of Queensland, Brisbane, Australia

    Azalden Alsger & Mark Hickman

  3. Australian Institute for Business and Economics, University of Queensland, Brisbane, Australia

    Mahboobeh Moghaddam

  4. Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

    Mahmoud Mesbah

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  1. Behrang Assemi
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  2. Azalden Alsger
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  3. Mahboobeh Moghaddam
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  4. Mark Hickman
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  5. Mahmoud Mesbah
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Correspondence to Behrang Assemi.

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Assemi, B., Alsger, A., Moghaddam, M. et al. Improving alighting stop inference accuracy in the trip chaining method using neural networks. Public Transp 12, 89–121 (2020). https://doi.org/10.1007/s12469-019-00218-9

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