Departure time choice behavior in commute problem with stochastic bottleneck capacity: experiments and modeling

The effect of environmental uncertainty on equilibrium patterns is of vital importance to understand travel choice behavior. This paper conducted a laboratory experiment to investigate the effects of stochastic bottleneck capacity on departure time choice behavior. In the experiment, the bottleneck...

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Published inTransportmetrica (Abingdon, Oxfordshire, UK) Vol. 19; no. 2
Main Authors Liu, Qiumin, Lu, Dongxu, Jiang, Rui, Han, Xiao, Liu, Ronghui, Gao, Ziyou
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 15.03.2023
Taylor & Francis Ltd
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Abstract The effect of environmental uncertainty on equilibrium patterns is of vital importance to understand travel choice behavior. This paper conducted a laboratory experiment to investigate the effects of stochastic bottleneck capacity on departure time choice behavior. In the experiment, the bottleneck capacity varied stochastically from round to round, and two different scenarios with different information feedback were investigated. Our experimental results showed that the relationship between the mean travel cost and the standard deviation of travel cost on each departure time was fitted approximately linearly with a positive slope , indicating subjects were more likely to minimize their travel cost budget rather than their mean travel cost. Also, we found that the feedback on costs of all departure times resulted in a smaller than the feedback on the subjects' own travel cost only. We propose a reinforcement learning model to reproduce the main experimental findings.
AbstractList The effect of environmental uncertainty on equilibrium patterns is of vital importance to understand travel choice behavior. This paper conducted a laboratory experiment to investigate the effects of stochastic bottleneck capacity on departure time choice behavior. In the experiment, the bottleneck capacity varied stochastically from round to round, and two different scenarios with different information feedback were investigated. Our experimental results showed that the relationship between the mean travel cost and the standard deviation of travel cost on each departure time was fitted approximately linearly with a positive slope , indicating subjects were more likely to minimize their travel cost budget rather than their mean travel cost. Also, we found that the feedback on costs of all departure times resulted in a smaller than the feedback on the subjects’ own travel cost only. We propose a reinforcement learning model to reproduce the main experimental findings.
The effect of environmental uncertainty on equilibrium patterns is of vital importance to understand travel choice behavior. This paper conducted a laboratory experiment to investigate the effects of stochastic bottleneck capacity on departure time choice behavior. In the experiment, the bottleneck capacity varied stochastically from round to round, and two different scenarios with different information feedback were investigated. Our experimental results showed that the relationship between the mean travel cost and the standard deviation of travel cost on each departure time was fitted approximately linearly with a positive slope , indicating subjects were more likely to minimize their travel cost budget rather than their mean travel cost. Also, we found that the feedback on costs of all departure times resulted in a smaller than the feedback on the subjects' own travel cost only. We propose a reinforcement learning model to reproduce the main experimental findings.
Author Han, Xiao
Liu, Ronghui
Liu, Qiumin
Jiang, Rui
Lu, Dongxu
Gao, Ziyou
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Snippet The effect of environmental uncertainty on equilibrium patterns is of vital importance to understand travel choice behavior. This paper conducted a laboratory...
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SubjectTerms bottleneck model
Departure time choice
Environment models
Feedback
laboratory experiment
reinforcement learning model
stochastic capacity
Title Departure time choice behavior in commute problem with stochastic bottleneck capacity: experiments and modeling
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