The application of a multi-operator smart card dataset to identify transfer locations with a high potential for transfer time loss minimization

The application of a multi-operator smart card dataset to identify transfer locations with a high potential for transfer time loss minimization

Ensing, Jorick (TU Delft Civil Engineering and Geosciences)

van Arem, B. (mentor)
van Oort, N. (mentor)
Annema, J.A. (mentor)
Scheltes, Arthur (mentor)
Tijs, Chris (mentor)

Delft University of Technology

Transport, Infrastructure and Logistics

2022-05-24

Travelers do not like transfers in their Public Transport journey; it gives a disutility. Minimizing this disutility is valuable to increase travelers' satisfaction. To be able to reduce this disutility, transfers have to be identified first. Multi-operator smart card datasets allow for the identification of transfers between different operators, as well as between the same operators. However, it is unknown in the literature how such a multi-operator smart card dataset can contribute to the minimization of transfer disutility in Public Transport. An answer is given by analyzing a multi-operator smart card dataset for the Haaglanden area in the Netherlands that uses a 35-minute time interval to identify transfers. Also, a measure has been implemented for one of the transfers with the highest potential for transfer time loss minimization and the effects on the network are examined in a transport model. It is found that a multi-operator smart card dataset can identify important transfer stations and individual transfers, for which the associated disutility factors can then be identified manually. Then, measures can be implemented to reduce the disutility of a transfer. The measure, a reduction of the waiting time, implemented on a transfer in this study resulted in changing traveler's route- and mode choices. For further research, it is recommended to explore the possible effects of implementing the measure as it can lead to crowding or other inefficient transfers, which could increase the disutility. Furthermore, further research should look into the disutility values of the case study to draw better conclusions whether, and to what degree, (dis)utility factors explain transfer flow sizes for this case specifically.

Public Transport
Transfers
Transfer penalty
(Dis)utility
Smart card data
Transport modeling

http://resolver.tudelft.nl/uuid:306f14dd-b430-4f60-a1e2-fb2f2f90c05f

Student theses

master thesis

© 2022 Jorick Ensing