A robust transfer inference algorithm for public transport journeys during disruptions

Yap, M.D.; Cats, O.; van Oort, N.; Hoogendoorn, S.P.

doi:10.1016/j.trpro.2017.12.099

A robust transfer inference algorithm for public transport journeys during disruptions

Title

A robust transfer inference algorithm for public transport journeys during disruptions

Author

Yap, M.D. (TU Delft Transport and Planning; Goudappel Coffeng)
Cats, O. (TU Delft Transport and Planning)
van Oort, N. (TU Delft Transport and Planning)
Hoogendoorn, S.P. (TU Delft Transport and Planning)

Date

2017

Abstract

Disruptions in public transport have major impact on passengers and disproportional effects on passenger satisfaction. The availability of smart card data gives opportunities to better quantify disruption impacts on passengers’ experienced journey travel time and comfort. For this, accurate journey inference from raw transaction data is required. Several rule-based algorithms exist to infer whether a passenger alighting and subsequent boarding is categorized as transfer or final destination where an activity is performed. Although this logic can infer transfers during undisrupted public transport operations, these algorithms have limitations during disruptions: disruptions and subsequent operational rescheduling measures can force passengers to travel via routes which would be non-optimal or illogical during undisrupted operations. Therefore, applying existing algorithms can lead to biased journey inference and biased disruption impact quantification. We develop and apply a new transfer inference algorithm which infers journeys from raw smart card transactions in an accurate way during both disrupted and undisrupted operations. In this algorithm we incorporate the effects of denied boarding, transferring to a vehicle of the same line (due to operator rescheduling measures as short-turning), and the use of public transport services of another operator on another network level as intermediate journey stage during disruptions. This results in an algorithm with an improved transfer inference performance compared to existing algorithms.

Subject

Disruptions
public transport
smart card data
transfer inference

To reference this document use:

http://resolver.tudelft.nl/uuid:d47787e3-5a97-4fd5-99c9-c680bf3fbff9

DOI

https://doi.org/10.1016/j.trpro.2017.12.099

Publisher

Elsevier

Source

20th EURO Working Group on Transportation Meeting, EWGT 2017, 27

Event

20th EURO Working Group on Transportation Meeting, 2017-09-04 → 2017-09-06, Budapest, Hungary