An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability

An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability

Geržinič, N. (TU Delft Transport and Planning)
Cats, O. (TU Delft Transport and Planning)
van Oort, N. (TU Delft Transport and Planning)
Hoogendoorn-Lanser, S. (TU Delft Corporate Innovations)
Bierlaire, Michel (Swiss Federal Institute of Technology)
Hoogendoorn, S.P. (TU Delft Transport and Planning)

Transport and Planning

2023

Understanding user's perception of service variability is essential to discern their overall perception of any type of (transport) service. We study the perception of waiting time variability for ride-hailing services. We carried out a stated preference survey in August 2021, yielding 936 valid responses. The respondents were faced with static pre-trip information on the expected waiting time, followed by the actually experienced waiting time for their selected alternative. We analyse this data by means of an instance-based learning (IBL) approach to evaluate how individuals respond to service performance variation and how this impacts their future decisions. Different novel specifications of memory fading, captured by the IBL approach, are tested to uncover which describes the user behaviour best. Additionally, existing and new specification of inertia (habit) are tested. Our model outcomes reveal that the perception of unexpected waiting time is within the expected range of 2–3 times the value-of-time. Travellers seem to place a higher reward on an early departure compared to a penalty for a late departure of equal magnitude. A cancelled service, after having made a booking, results in significant disutility for the passenger and a strong motivation to shift to a different provider. Considering memory decay, our results show that the most recent experience is by far the most relevant for the next decision, with memories fading quickly in importance. The role of inertia seems to gain importance with each additional consecutive choice for the same option, but then resetting back to zero following a shift in behaviour.

Choice modelling
Instance-based learning
Memory decay
Ride-hailing
Service reliability
Waiting time

http://resolver.tudelft.nl/uuid:a1e2710f-e788-4cd0-8d76-bef74460907c

https://doi.org/10.1016/j.tbs.2023.100616

2214-367X

Travel Behaviour and Society, 33

Institutional Repository

journal article

© 2023 N. Geržinič, O. Cats, N. van Oort, S. Hoogendoorn-Lanser, Michel Bierlaire, S.P. Hoogendoorn