Modelling cyclist queue formation using a two-layer framework for operational cycling behaviour

Journal Article (2019)
Author(s)

A. Gavriilidou (TU Delft - Transport and Planning)

W. Daamen (TU Delft - Transport and Planning)

Y Yuan (TU Delft - Transport and Planning)

Serge Paul Hoogendoorn (TU Delft - Transport and Planning)

Transport and Planning
Copyright
© 2019 A. Gavriilidou, W. Daamen, Y. Yuan, S.P. Hoogendoorn
DOI related publication
https://doi.org/10.1016/j.trc.2019.06.012
More Info
expand_more
Publication Year
2019
Language
English
Copyright
© 2019 A. Gavriilidou, W. Daamen, Y. Yuan, S.P. Hoogendoorn
Transport and Planning
Bibliographical Note
Accepted Author Manuscript@en
Volume number
105
Pages (from-to)
468-484
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Operational cycling behaviour is greatly understudied, even lacking a definition of what this behavioural level actually entails in terms of decision making. In this paper, we define the cyclist operational level and argue that it consists of two intertwined processes, a mental and a physical process. The mental process refers to path choices made within a route and the physical process refers to the bicycle control dynamics through pedalling and steering. We propose a novel two-layer framework, where each layer captures the tasks of one of the processes within the operational level. Discrete choice theory is proposed to model each layer. The plausibility of the framework is demonstrated through an application focusing on the queue formation process upstream of a red traffic light, including selecting a queuing position and cycling towards it. Models are estimated for the two layers using cyclist trajectory data collected at a signalised intersection in Amsterdam, the Netherlands. The models reveal the attributes that influence the decisions made in each layer and are face validated using simulation. The proposed framework and the (behavioural) findings of its application are the main scientific contributions of this paper, which pave the way for future research.

Files

TRC_Gavriilidou_et_al_2019.pdf
(pdf | 3.08 Mb)
- Embargo expired in 28-06-2021