Data-driven public transport ridership prediction approach including comfort aspects

Niels van Oort, Marc Drost, Ties Brands, Menno Yap

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

Abstract

The most important aspects on which passengers base their choice whether to travel by public transport are the perceived travel time, costs, reliability and comfort. Despite its importance, comfort is often not explicitly considered when predicting demand for public transport. In this paper, we include comfort level in a modelling framework by incorporating capacity in the public transport assignment. This modelling framework is applied in the public transport model of HTM, the urban public transport operator of The Hague. The current transportation demand is directly derived from smart card data and future demand is estimated using an elasticity based approach. The case study results indicate that not considering capacity and comfort effects can lead to a substantial underestimation of effects of certain measures aiming to improve public transport (up to 30%). We also illustrate that this extended modelling framework can be applied in practice: it has a short computation time and leads to better predictions of public transport demand.
Original languageEnglish
Title of host publicationProceedings of Conference on Advanced Systems in Public Transport, 19-23 July 2015, Rotterdam. On USB stick.
Place of PublicationRotterdam
PublisherCASPT
Number of pages13
Publication statusPublished - 19 Jul 2015
EventConference on Advanced Systems in Public Transport, CASPT 2015 - Rotterdam, Netherlands
Duration: 19 Jul 201523 Jul 2015

Conference

ConferenceConference on Advanced Systems in Public Transport, CASPT 2015
Abbreviated titleCASPT
CountryNetherlands
CityRotterdam
Period19/07/1523/07/15

Fingerprint

Smart cards
Travel time
Elasticity
Costs

Cite this

van Oort, N., Drost, M., Brands, T., & Yap, M. (2015). Data-driven public transport ridership prediction approach including comfort aspects. In Proceedings of Conference on Advanced Systems in Public Transport, 19-23 July 2015, Rotterdam. On USB stick. Rotterdam: CASPT.
van Oort, Niels ; Drost, Marc ; Brands, Ties ; Yap, Menno. / Data-driven public transport ridership prediction approach including comfort aspects. Proceedings of Conference on Advanced Systems in Public Transport, 19-23 July 2015, Rotterdam. On USB stick.. Rotterdam : CASPT, 2015.
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abstract = "The most important aspects on which passengers base their choice whether to travel by public transport are the perceived travel time, costs, reliability and comfort. Despite its importance, comfort is often not explicitly considered when predicting demand for public transport. In this paper, we include comfort level in a modelling framework by incorporating capacity in the public transport assignment. This modelling framework is applied in the public transport model of HTM, the urban public transport operator of The Hague. The current transportation demand is directly derived from smart card data and future demand is estimated using an elasticity based approach. The case study results indicate that not considering capacity and comfort effects can lead to a substantial underestimation of effects of certain measures aiming to improve public transport (up to 30{\%}). We also illustrate that this extended modelling framework can be applied in practice: it has a short computation time and leads to better predictions of public transport demand.",
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year = "2015",
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van Oort, N, Drost, M, Brands, T & Yap, M 2015, Data-driven public transport ridership prediction approach including comfort aspects. in Proceedings of Conference on Advanced Systems in Public Transport, 19-23 July 2015, Rotterdam. On USB stick.. CASPT, Rotterdam, Conference on Advanced Systems in Public Transport, CASPT 2015, Rotterdam, Netherlands, 19/07/15.

Data-driven public transport ridership prediction approach including comfort aspects. / van Oort, Niels; Drost, Marc; Brands, Ties; Yap, Menno.

Proceedings of Conference on Advanced Systems in Public Transport, 19-23 July 2015, Rotterdam. On USB stick.. Rotterdam : CASPT, 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

TY - GEN

T1 - Data-driven public transport ridership prediction approach including comfort aspects

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AU - Yap, Menno

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N2 - The most important aspects on which passengers base their choice whether to travel by public transport are the perceived travel time, costs, reliability and comfort. Despite its importance, comfort is often not explicitly considered when predicting demand for public transport. In this paper, we include comfort level in a modelling framework by incorporating capacity in the public transport assignment. This modelling framework is applied in the public transport model of HTM, the urban public transport operator of The Hague. The current transportation demand is directly derived from smart card data and future demand is estimated using an elasticity based approach. The case study results indicate that not considering capacity and comfort effects can lead to a substantial underestimation of effects of certain measures aiming to improve public transport (up to 30%). We also illustrate that this extended modelling framework can be applied in practice: it has a short computation time and leads to better predictions of public transport demand.

AB - The most important aspects on which passengers base their choice whether to travel by public transport are the perceived travel time, costs, reliability and comfort. Despite its importance, comfort is often not explicitly considered when predicting demand for public transport. In this paper, we include comfort level in a modelling framework by incorporating capacity in the public transport assignment. This modelling framework is applied in the public transport model of HTM, the urban public transport operator of The Hague. The current transportation demand is directly derived from smart card data and future demand is estimated using an elasticity based approach. The case study results indicate that not considering capacity and comfort effects can lead to a substantial underestimation of effects of certain measures aiming to improve public transport (up to 30%). We also illustrate that this extended modelling framework can be applied in practice: it has a short computation time and leads to better predictions of public transport demand.

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van Oort N, Drost M, Brands T, Yap M. Data-driven public transport ridership prediction approach including comfort aspects. In Proceedings of Conference on Advanced Systems in Public Transport, 19-23 July 2015, Rotterdam. On USB stick.. Rotterdam: CASPT. 2015