Analysing and modelling of influencing attributes on transfer walking times for metro transfers

Master Thesis (2023)
Author(s)

B.A.M. van Voorst tot Voorst (TU Delft - Civil Engineering & Geosciences)

Contributor(s)

W. Daamen – Graduation committee member (TU Delft - Transport and Planning)

Y. Yuan – Graduation committee member (TU Delft - Transport and Planning)

Niels van Van Oort – Graduation committee member (TU Delft - Transport and Planning)

Laura Pardini Susacasa – Graduation committee member (Royal HaskoningDHV)

Barth Donners – Graduation committee member (Royal HaskoningDHV)

Nigel Birch – Mentor (Royal HaskoningDHV)

Faculty
Civil Engineering & Geosciences
Copyright
© 2023 B.A.M. van Voorst tot Voorst
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 B.A.M. van Voorst tot Voorst
Graduation Date
31-05-2023
Awarding Institution
Delft University of Technology
Programme
['Civil Engineering | Transport and Planning']
Faculty
Civil Engineering & Geosciences
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Abstract

This research aims to understand the influencing attributes of passengers and station layout elements on transfer walking times for metro transfers. Little research has been performed to include passenger-related attributes to model transfer walking time besides station layout elements. Through a literature review, the effect of gender, luggage size, group size and level of crowding are potential attributes. Furthermore, the vertical transport mode choice, including the lift, the waiting condition to board a vertical transport mode and the alighting location, are also part of the influential attributes besides the transfer length. The walking time and passenger characteristics have been collected through a covert observation. In the data analysis, the effect of group size, vertical transport mode choice, waiting condition to board and the alighting location significantly impact the transfer walking time and the walking time on a transfer segment level. These attributes have been captured in walking time and passing speed models for various transfer segment types. The walking time models can predict a lower, mean and upper bound of the walking time for each combination of attributes. The case study for the walking time collection was metro station Beurs, Rotterdam.

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