A review of methods to model route choice behavior of bicyclists
Year: 2020
Type: conference-paper
Abstract: Used for route choice modeling by the transportation research community, recursive logit is a form of inverse reinforcement learning, the field of learning an agent's objective by observing it's behavior. By solving a large-scale system of linear equations it allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of IRL models applied to real world travel trajectories and look at some of the challenges with recursive logit for modeling bicycle route choice in the city center area of Amsterdam.
Authors: Thomas Koch, Elenna Dugundji
Institutions: Centrum Wiskunde & Informatica, Vrije Universiteit Amsterdam
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3097667296