Modeling Concurrent Day-to-Day Departure Time and Route Choices With Multiple Micro-Preferences

Day-to-day traffic dynamics is to model the day-to-day evolution of travelers' travel choices, which helps to understand the aggregate traffic evolution of a non-equilibrium network and then develop scientific managements. This topic is attracting increasing interests. In the literature, day-to...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.198845-198855
Main Authors: Zhang, Wenyi, Kou, Zhao, Xia, Dongyang, Hao, Jingyi, Jiang, Shixiong
Format: Article
Language:English
Subjects:
Aggregates
behavior preference
Communications traffic
Day-to-day traffic dynamics
departure time choice
Evolution
Microscopy
Numerical models
route choice
Telecommunication traffic
Traffic models
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Summary:Day-to-day traffic dynamics is to model the day-to-day evolution of travelers' travel choices, which helps to understand the aggregate traffic evolution of a non-equilibrium network and then develop scientific managements. This topic is attracting increasing interests. In the literature, day-to-day route and departure time choices are usually addressed separately; and the existing models commonly adhere to the rational behavior adjustment criterion (RBAC), but pay little attention to extra behavior preferences. In this article, we formulate the day-to-day departure time and route choices in a united model. Moreover, besides the RBAC, three microscopic behavior preferences (i.e., simplicity-seeking, proximity-prone and marginal cost preference) are suggested for modeling. The problem is formulated as a discrete-time dynamics named by the day-to-day departure time and route adjustment process (DTRAP). Basic properties of the model are verified theoretically; and numerical results indicate that the suggested micro-preferences have significant impact on traffic evolution. Thus, serious treatment and more in-depth research attention need to be laid on these micro-preferences for obtaining scientific understanding on the day-to-day network traffic evolution or for avoiding ineffective (or even wrong) traffic managements.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3034873