Stochastic Network User Equilibrium and Traffic System Evolution Based on Reference-Dependent Utility Theory
This paper applies the reference-dependent utility theory (RDUT) to model traveler's route choice behaviors under travel time variability and develops a user equilibrium (UE) model based on RDUT for stochastic traffic networks. The proposed model explicitly considers both the absolute utility (...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8633825/ |
| _version_ | 1818416124704849920 |
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| author | Wei Wang Xiujuan Liu Lili Ding Ge Gao Hui Zhang |
| author_facet | Wei Wang Xiujuan Liu Lili Ding Ge Gao Hui Zhang |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | This paper applies the reference-dependent utility theory (RDUT) to model traveler's route choice behaviors under travel time variability and develops a user equilibrium (UE) model based on RDUT for stochastic traffic networks. The proposed model explicitly considers both the absolute utility (or consumption utility) and the relative utility (or gain-loss utility) in the travelers' path choice decision procedure. The former is determined by the stochastic path travel time while the latter is measured by the actual path travel time relative to a reference time point. Subsequently, the RDUT-based UE model, which can be equivalently formulated as a variational inequality problem and solved by a heuristic algorithm, is employed to explore how risk aversion and reference-dependent preference jointly determine network equilibrium patterns. Both the features and applicability of the proposed RDUT-UE model and the designed solution algorithm are demonstrated in two numerical examples. This paper further establishes an RDUT-based dynamic traffic system which incorporates commuters' learning, choosing, and renewing process in dynamic path choices and captures the day-to-day dynamic traffic flows. Another numerical example is presented to show how the dynamic traffic system evolves to the UE status. |
| first_indexed | 2024-12-14T11:45:54Z |
| format | Article |
| id | doaj.art-ecc886f3f82f4050b0b9d4ddfb2fe6db |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:45:54Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ecc886f3f82f4050b0b9d4ddfb2fe6db2022-12-21T23:02:36ZengIEEEIEEE Access2169-35362019-01-017198661988010.1109/ACCESS.2019.28968798633825Stochastic Network User Equilibrium and Traffic System Evolution Based on Reference-Dependent Utility TheoryWei Wang0Xiujuan Liu1Lili Ding2https://orcid.org/0000-0003-1915-1127Ge Gao3Hui Zhang4School of Economics, Ocean University of China, Qingdao, ChinaSchool of Economics, Ocean University of China, Qingdao, ChinaSchool of Economics, Ocean University of China, Qingdao, ChinaCollege of Transportation, Shandong University of Science and Technology, Qingdao, ChinaSchool of Transportation Engineering, Shandong Jianzhu University, Jinan, ChinaThis paper applies the reference-dependent utility theory (RDUT) to model traveler's route choice behaviors under travel time variability and develops a user equilibrium (UE) model based on RDUT for stochastic traffic networks. The proposed model explicitly considers both the absolute utility (or consumption utility) and the relative utility (or gain-loss utility) in the travelers' path choice decision procedure. The former is determined by the stochastic path travel time while the latter is measured by the actual path travel time relative to a reference time point. Subsequently, the RDUT-based UE model, which can be equivalently formulated as a variational inequality problem and solved by a heuristic algorithm, is employed to explore how risk aversion and reference-dependent preference jointly determine network equilibrium patterns. Both the features and applicability of the proposed RDUT-UE model and the designed solution algorithm are demonstrated in two numerical examples. This paper further establishes an RDUT-based dynamic traffic system which incorporates commuters' learning, choosing, and renewing process in dynamic path choices and captures the day-to-day dynamic traffic flows. Another numerical example is presented to show how the dynamic traffic system evolves to the UE status.https://ieeexplore.ieee.org/document/8633825/Reference-dependent utility theoryuser equilibriumabsolute utilityrelative utilitysystem evolution |
| spellingShingle | Wei Wang Xiujuan Liu Lili Ding Ge Gao Hui Zhang Stochastic Network User Equilibrium and Traffic System Evolution Based on Reference-Dependent Utility Theory IEEE Access Reference-dependent utility theory user equilibrium absolute utility relative utility system evolution |
| title | Stochastic Network User Equilibrium and Traffic System Evolution Based on Reference-Dependent Utility Theory |
| title_full | Stochastic Network User Equilibrium and Traffic System Evolution Based on Reference-Dependent Utility Theory |
| title_fullStr | Stochastic Network User Equilibrium and Traffic System Evolution Based on Reference-Dependent Utility Theory |
| title_full_unstemmed | Stochastic Network User Equilibrium and Traffic System Evolution Based on Reference-Dependent Utility Theory |
| title_short | Stochastic Network User Equilibrium and Traffic System Evolution Based on Reference-Dependent Utility Theory |
| title_sort | stochastic network user equilibrium and traffic system evolution based on reference dependent utility theory |
| topic | Reference-dependent utility theory user equilibrium absolute utility relative utility system evolution |
| url | https://ieeexplore.ieee.org/document/8633825/ |
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