@article{oai:kanazawa-u.repo.nii.ac.jp:00009879,
 author = {Nakayama, Shoichiro and 中山, 晶一朗},
 issue = {1},
 journal = {土木学会論文集D},
 month = {Jan},
 note = {In this study, we assume that each driver under day-to-day dynamic transportation circumstances chooses a route based on Bayesian learning, and develop a day-to-day dynamical model of network flow. It is found in this model that the driver with Bayesian learning chooses the route which has the minimum travel time the most frequently. Furthermore, we find that an equilibrium point of the day-to-day dynamical model is identical to the Wardrop's equilibrium, and the Wardrop's equilibrium is globally asymptotically stable if initial recognition among drivers is dispersed widely, and the day-to-day dynamical system always converges to the Wordrop's equilibrium., 金沢大学融合研究域融合科学系 / 金沢大学理工研究域環境デザイン学系},
 pages = {1--11},
 title = {経路選択行動のday-to-dayダイナミクスと交通ネットワーク均衡の形成プロセス},
 volume = {65},
 year = {2009}
}