A Modification of Multiple Discrete-Continuous (MDC) Choice Model to Consider Nonmonotonic Preference in Episode-Level Time-Use Behaviors
The multiple discrete-continuous extreme value model with ordered preferences (MDCEV-OP) has broad prospects in activity-based modeling (ABM) since it can model episode-level time-use decisions and ensure a logical prediction across different episodes of an activity. However, the current MDCEV-OP fr...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/atr/7114605 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850141331576324096 |
|---|---|
| author | Mengyi Wang Xin Ye Ke Wang |
| author_facet | Mengyi Wang Xin Ye Ke Wang |
| author_sort | Mengyi Wang |
| collection | DOAJ |
| description | The multiple discrete-continuous extreme value model with ordered preferences (MDCEV-OP) has broad prospects in activity-based modeling (ABM) since it can model episode-level time-use decisions and ensure a logical prediction across different episodes of an activity. However, the current MDCEV-OP framework assumes a monotonically increasing utility function for each episode alternative, which fails to accommodate potential nonmonotonic preference in episode-level time consumption. In this paper, we modify the traditional MDCEV-OP model by adding a baseline marginal utility parameter, making the model more flexible to reflect the potential nonmonotonic preference in episode-level time-use behaviors, as well as ensuring the logically consistent prediction as in the traditional model. To our knowledge, it is the first time to develop an episode-level MDCEV model that considers nonmonotonic preference. The new MDCEV-OP model was applied to analyze the episode-level time-use pattern of noncommuters in Shanghai, China. The empirical results show that the new model provides plausible explanations for nonmonotonic preference in episode-level time-use behaviors and outperforms the traditional model both in data fitting and forecasting performance. |
| format | Article |
| id | doaj-art-3c4569c1a8384be08931b2e0741d60bb |
| institution | OA Journals |
| issn | 2042-3195 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-3c4569c1a8384be08931b2e0741d60bb2025-08-20T02:29:29ZengWileyJournal of Advanced Transportation2042-31952025-01-01202510.1155/atr/7114605A Modification of Multiple Discrete-Continuous (MDC) Choice Model to Consider Nonmonotonic Preference in Episode-Level Time-Use BehaviorsMengyi Wang0Xin Ye1Ke Wang2Key Laboratory of Road and Traffic Engineering of Ministry of EducationKey Laboratory of Road and Traffic Engineering of Ministry of EducationBusiness SchoolThe multiple discrete-continuous extreme value model with ordered preferences (MDCEV-OP) has broad prospects in activity-based modeling (ABM) since it can model episode-level time-use decisions and ensure a logical prediction across different episodes of an activity. However, the current MDCEV-OP framework assumes a monotonically increasing utility function for each episode alternative, which fails to accommodate potential nonmonotonic preference in episode-level time consumption. In this paper, we modify the traditional MDCEV-OP model by adding a baseline marginal utility parameter, making the model more flexible to reflect the potential nonmonotonic preference in episode-level time-use behaviors, as well as ensuring the logically consistent prediction as in the traditional model. To our knowledge, it is the first time to develop an episode-level MDCEV model that considers nonmonotonic preference. The new MDCEV-OP model was applied to analyze the episode-level time-use pattern of noncommuters in Shanghai, China. The empirical results show that the new model provides plausible explanations for nonmonotonic preference in episode-level time-use behaviors and outperforms the traditional model both in data fitting and forecasting performance.http://dx.doi.org/10.1155/atr/7114605 |
| spellingShingle | Mengyi Wang Xin Ye Ke Wang A Modification of Multiple Discrete-Continuous (MDC) Choice Model to Consider Nonmonotonic Preference in Episode-Level Time-Use Behaviors Journal of Advanced Transportation |
| title | A Modification of Multiple Discrete-Continuous (MDC) Choice Model to Consider Nonmonotonic Preference in Episode-Level Time-Use Behaviors |
| title_full | A Modification of Multiple Discrete-Continuous (MDC) Choice Model to Consider Nonmonotonic Preference in Episode-Level Time-Use Behaviors |
| title_fullStr | A Modification of Multiple Discrete-Continuous (MDC) Choice Model to Consider Nonmonotonic Preference in Episode-Level Time-Use Behaviors |
| title_full_unstemmed | A Modification of Multiple Discrete-Continuous (MDC) Choice Model to Consider Nonmonotonic Preference in Episode-Level Time-Use Behaviors |
| title_short | A Modification of Multiple Discrete-Continuous (MDC) Choice Model to Consider Nonmonotonic Preference in Episode-Level Time-Use Behaviors |
| title_sort | modification of multiple discrete continuous mdc choice model to consider nonmonotonic preference in episode level time use behaviors |
| url | http://dx.doi.org/10.1155/atr/7114605 |
| work_keys_str_mv | AT mengyiwang amodificationofmultiplediscretecontinuousmdcchoicemodeltoconsidernonmonotonicpreferenceinepisodeleveltimeusebehaviors AT xinye amodificationofmultiplediscretecontinuousmdcchoicemodeltoconsidernonmonotonicpreferenceinepisodeleveltimeusebehaviors AT kewang amodificationofmultiplediscretecontinuousmdcchoicemodeltoconsidernonmonotonicpreferenceinepisodeleveltimeusebehaviors AT mengyiwang modificationofmultiplediscretecontinuousmdcchoicemodeltoconsidernonmonotonicpreferenceinepisodeleveltimeusebehaviors AT xinye modificationofmultiplediscretecontinuousmdcchoicemodeltoconsidernonmonotonicpreferenceinepisodeleveltimeusebehaviors AT kewang modificationofmultiplediscretecontinuousmdcchoicemodeltoconsidernonmonotonicpreferenceinepisodeleveltimeusebehaviors |