（通讯员：杨莹莹 审核人：齐航）近日，我院齐航副教授以第一作者身份、以湖北经济学院为第一机构的论文《Investigating Day-to-day Route Choices Based on Multi-Scenario Laboratory Experiments. Part II: A route-dependent attraction-based stochastic process model》在中科院一区TOP期刊《Communications in Transportation Research》在线发表。据悉，该期刊影响因子达12.5，JCR一区，位居Transportation（交通）学科全球58种期刊榜首（1/58）。该论文是作者团队利用实验经济学方法探究交通选择行为系列论文之一，前作发表行为经济学顶尖期刊之一《Journal of Economic Behavior & Organization》（我校英文A类）、运输经济中科院一区TOP期刊《Transportation Research Part A: Policy and Practice》等国际期刊上。
摘要：Laboratory experiments are one of the important means used to investigate travel choice behavior under strategic uncertainty. Many experiment-based studies have shown that the Nash equilibrium can predict aggregated route choices, while the fluctuations, whose mechanisms are still unclear, continue to exist until the end. To understand the fluctuations, this paper proposes a route-dependent attraction-based stochastic process model, which shares exactly the same behavioral foundation introduced in Part I of the study (Qi et al., 2023), i.e., route-dependent inertia and route-dependent preference. The model predictions are carefully compared with the experimental observations obtained from the congestible parallel-route laboratory experiments containing 312 subjects and eight decision-making scenarios (Qi et al., 2023). The results show that the proposed stochastic process model can precisely reproduce the random oscillations both in terms of flow switching and route flow evolution. Subsequently, an approximated model is developed to enhance the efficiency in evaluating the equilibrium distribution, providing a practical tool to evaluate the impacts of transportation policies in both long- and short-term runs. To the best of our knowledge, this paper is the first attempt to model and explain experimental phenomena by introducing stochastic process theories, as well as a successful example of applying experimental economics methodology to improve our understanding of human travel choice behavior.
文章DOI：https://doi.org/10.1016/j.commtr.2024.100123
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