Decision dynamics of departure times: Experiments and modeling

In a transportation system with multiple equilibria, coordination failure occurs when a group of travelers could achieve the efficient equilibrium but fail because they cannot coordinate their decision‐making under strategic uncertainty. This study investigated factors that might affect coordination behavior in mode choice with both positive and negative externalities. Subjects participated in a mode‐choice game in which they were required to choose between public transport and private car. Positive or negative externalities occurred when subjects chose public transport or private car, respectively, thereby generating two pure‐strategy equilibria in the mode‐choice game. The experimental results indicated that (i) the transparency of descriptive information facilitated not only the convergence to the efficient equilibrium but also the stabilization of choice behavior, (ii) historical experiences of converging to the inefficient equilibrium could affect subjects' initial choices and even equilibrium selection, and (iii) a “higher” barrier between the two equilibria impeded and prolonged the transformation from the inefficient equilibrium to the efficient equilibrium, and a “deeper” attraction basin of the efficient equilibrium was more attractive in equilibrium selection.

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“…2006, Ramadurai and Ukkusuri 2007, Sun et al. 2017, Ziegelmeyer et al. 2008), and mode choice (Chidambaram et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Coordination Behavior in Mode Choice: Laboratory Study of Equilibrium Transformation and Selection

Han

Jia

et al. 2021

Production and Operations Management

Self Cite

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“…e Reinforcement Learning Model. Reinforcement learning has been widely used to develop decision behavior models [21,24,25]. In reinforcement learning, the benefits of a strategy selected in the previous round will be used to reinforce the tendency to choose that strategy, and the tendency to choose a different strategy will be converted into the probability of that strategy.…”

Section: 21mentioning

confidence: 99%

“…Faced with a risky choice leading to gains, individuals are riskaverse; while faced with a risky choice leading to losses, individuals are risk-seeking [29,30]. Fermi rule: Fermi rule originated from condensed matter physics and is widely used in decision modelling in evolutionary games [21,31]. Compared with the general reinforcement learning model, the number of adjustable parameters using Fermi rule is reduced by one.…”

Section: E Fermi Learning Modelmentioning

confidence: 99%

“…A multiple trip negotiation model is introduced in [19], where flexible activity scheduling is considered between a small group of agents. Flexible trip departure times are also studied in theory and experiment [20,21]. e multiagent model is also introduced in taxi-sharing [22], considering drivers' multiple preferences and taxi dispatch [23] with reduction in customer waiting and empty taxi cruising times.…”

Section: Introductionmentioning

confidence: 99%

“…We propose a new multiagent model that models both passengers and drivers as autonomous agents and conduct multiagents simulations on top of reinforcement learning model [21,24,25] and prospect theory [26][27][28][29][30] based Fermi learning model [21,31]. e remainder of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Characteristics and Self-Organization of Urban Taxi Dispatch

Zhang

Fan

2020

Complexity

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This paper proposes a matching degree to study dynamic spatiotemporal characteristics of urban taxi and offers a novel understanding of self-organization taxi dispatch in hotspots on top of the Fermi learning model. The proposed matching degree can not only reflect the overall spatiotemporal characteristics of urban taxi supply and demand but also show that the density of distribution and the distance between the taxis supply and the city center will affect the satisfaction of demand. Besides, it is interesting to note that supply always exceeds demand and they will self-organize into an equilibrium state in hotspots. To understand the phenomenon, we develop the Fermi learning model based on the prospect theory and compared the results with the popular reinforcement learning model. The results demonstrate that both models can account for self-organization behavior under different scenarios. We believe our work is crucial to explore taxis data and our indicator can provide a significant suggestion for urban taxis development.

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Experiment on Destination Choice Game

Cai

2020

Green, Smart and Connected Transportation Systems

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Decision dynamics of departure times: Experiments and modeling

Cited by 15 publications

References 35 publications

Coordination Behavior in Mode Choice: Laboratory Study of Equilibrium Transformation and Selection

Coordination Behavior in Mode Choice: Laboratory Study of Equilibrium Transformation and Selection

Spatiotemporal Characteristics and Self-Organization of Urban Taxi Dispatch

Experiment on Destination Choice Game

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