原文传递
Multinomial Probit with Structured Covariance for Choice Situations with Similar Alternatives
| 题名： | Multinomial Probit with Structured Covariance for Choice Situations with Similar Alternatives |
| 作者： | Yai-T; Shimizu-T |
| 关键词： | Multinomials; Choice models; Probits; Covariance; Route choice; Origin and destination; Periods of the day; Mode choice; Tokyo (Japan); Multinomial probit models; Structured covariances; Destination; Time of day |
| 摘要： | The estimatability of the multinomial probit (MNP) model has been improved greatly by the modeling of the error structure. A modeling idea for travelers' choice behaviors using the MNP with structured covariances (MNPSC) model is proposed. Choice of route, destination, and time of day are considered. The covariance of the error term of the utility function is structured by introducing the overlapped length between any two routes and the overlapped time length between any two departure times. Simultaneous models such as route and train class, route and mode, and time of day and mode choice model also are introduced by applying the MNPSC modeling idea. These are formulated easily by assuming that the error is generated independently between two choices. An empirical study for a route and train class choice model was conducted by using the passenger survey data in the Tokyo metropolitan region, and its estimatability and applicability are examined. |
| 总页数： | Transportation Research Record.1998. pp 69-75 (FIGS: 2 Fig. TABS: 1 Tab. REFS: 9 Ref.) |
| 报告类型： | 科技报告 |
相关文献
- Application of a Rank Ordered Probit Modeling Approach to Understanding Level of Interest in Autonomous Vehicles.
- Efficiently Estimating Nested Logit Models with Choice-Based Samples: Example Applications.
- STOCHASTIC USER EQUILIBRIUM FORMULATION FOR GENERALIZED NESTED LOGIT MODEL.
- Nested Logit Models Which Are You Using?
- AUTOMOBILE OWNERSHIP ANALYSIS USING ORDERED PROBIT MODELS.
- ANALYSIS OF DISCRETE CHOICE DATA WITH REPEATED OBSERVATIONS: COMPARISON OF THREE TECHNIQUES IN INTERCITY TRAVEL CASE.
检索历史
应用推荐
