• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.87-97
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• 2007

This paper proposes drivers' rational belief formation under a bounded traffic environment. This is to escape the criticism that excessive rationality (e.g., a driver's calculating ability and memory capacity) is required of drivers. Under bounded traffic environments. drivers do not have structural knowledge of traffic conditions and others' decisions. Simulations are carried out using a program coded in C. Consequently, the author found the learning process of drivers and the value of information can be differentiated by route conditions and the characteristics of driver groups. Also, it was found that rational drivers form different beliefs about traffic conditions even though they have the same traffic environment in a bounded traffic environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.14-25
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• 2020

This research proposes a methodology to evaluate the rationality of passengers' route choice who make trips within Seoul metropolitan subway based on smart card data. The rationality of user route choice is divided into the degree of determinacy and similarity concepts as basic principle. Determinacy is the degree to which the route selected by the passenger is identical to the system optimal path. Similarity indicates the degree to which the route is similar to the system optimal path. The K-path search method is used for path enumeration, which allows for measurement of determinacy. To assess determinacy within similarity, transfer tag data of private operators is used. Consequently, the concept of similarity applied to the model is such that the passenger's path choice is identical to the path taken using the tag reader. Results show that the determinacy of appearance of the shortest path (K=1) is 90.4%, while the similarity of appearance as K=(2-10) is 7.9%, summing to 98.3%. This indicates that trips on the metropolitan subway network are being rationally explained. 1.7% of irrational trips are attributed to the unexplainable error term that occurs due to the diversity of passengers.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.97-106
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• 2003

When a route choice is done under uncertainty, a driver has some expectation of traffic conditions that will occur according to the route chosen. This study tries to build a framework in which we can observe the learning behavior of the drivers' expectations of the travel time under nonstationary environment. In order to investigate how drivers have their subjective expectations on traffic conditions in response to public information, a numerical experiment is carried out. We found that rational expectations(RE) formation about the route travel time can be expressed by the adaptive expectation model when the travel time changes in accordance with the nonstationary process which consists of permanent shock and transient shock. Also, we found that the adaptive parameter of the model converges to the fixed value corresponding to the route conditions.

• International Journal of Highway Engineering
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• v.13 no.2
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• pp.195-202
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• 2011

This study develops a day-to-day dynamics modeling framework, incorporating a consistent drivers' travel time perception behavior and traffic information provision. Descriptive traffic information is updated and provided to the subscribers making a final decision on route choice. Nonsubscribers(not equipped any information devices) are assumed to obtain daily traffic information from their experience or friends or other public agencies. Drivers' route choice behavior is modeled based on boundedly-rational behavior rules. A microscopic traffic simulation model is adopted to evaluate the network system performance. Numerical experiments on a real world network have demonstrated the convergent property of the proposed model and the effectiveness of the consistent perception model.