Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/30241
Title: Path complexity and bicyclist route choice set quality assessment
Authors: Koch, Thomas
KNAPEN, Luk 
Dugundji, Elenna
Issue Date: 2021
Publisher: Springer
Source: Personal and Ubiquitous Computing, 25(1), p. 63-75
Abstract: Everyday route choices made by bicyclists are known to be more difficult to explain than vehicle routes, yet prediction of these choices is essential for guiding infrastructural investment in safe cycling. Building route choice sets is a difficult task. Even including detailed attributes such as the number of left turns, the number of speed bumps, distance and other route choice properties we still see that choice set quality measures suggest poor replication of observed paths. In this paper we study how the concept of route complexity can help generate and analyze plausible choice sets in the demand modeling process. The complexity of a given path in a graph is the minimum number of shortest paths that is required to specify that path. Complexity is a path attribute which could potentially be considered to be important for route choice in a similar way. The complexity was determined for a large set of observed routes and for routes in the generated choice sets for the corresponding origin-destination pairs. The respective distributions are shown to be significantly different so that the choice sets do not reflect the traveler preferences, this is in line with classical choice set quality indicators. Secondly, we investigate often used choice set quality methods and formulate measures that are less sensitive to small differences between routes that can be argued to be insignificant or irrelevant. Such difference may be partially due to inaccuracy in map-matching observations to dense urban road networks.
Keywords: Route choice generation;Choice sets;Route complexity
Document URI: http://hdl.handle.net/1942/30241
ISSN: 1617-4909
e-ISSN: 1617-4917
DOI: 10.1007/s00779-019-01350-w
ISI #: 000574555100001
Rights: Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/.
Category: A1
Type: Journal Contribution
Validations: ecoom 2022
Appears in Collections:Research publications

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