Data · dataset · 2006
A Study of the Egress Modes Affects to the Domestic Air and High Speed Rail Passengers
Listed in Harvard Dataverse
The Domestic airports and the High Speed Rail (HSR) stations are almost far from the city center, so they have the problems of the location and need to the egress modes to carry the passengers to the destination.
Description
The study constructs the intercity mode choice models and the egress mode models to discuss the variables and explain the passenger behavior from origin to destination. The empirical study is the Taipei-Tainan intercity trip.
We survey the respondents from several intercity mode stations in Tainan. The research method is the Nested Multinomial Logit Model (NMNL) with revealed preference data (RP) and stated preference data (SP). We use two stage choice nest concept, the lower nest is the egress mode choice and the upper one is the intercity mode choice.
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The NMNL model traditionally calculates the inclusive value in the lower nest as an explanatory variable to enter the upper one. In this paper, we also discuss several concepts to calculate different values as the egress service explanatory variable. The empirical result shows the main variables like as the travel time, travel cost, waiting time, income, occupation, assess service attributes.
That means the advantage egress mode service can significantly affect the passengers preference for the intercity mode choice. Evaluating the Air or HSR egress service strategies, we can suggest some opinions to domestic air and HSP company to consider the important of the egress mode service.
Links
Where it is published
- Dataverse dataset page dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.6141%2FTW-SRDA-E93046-1 ↗
landing page · from Harvard Dataverse
- DOI doi.org/10.6141/tw-srda-e93046-1 ↗
DOI / persistent id · from Harvard Dataverse
Catalogue records · 1
- Dataverse API dataverse.harvard.edu/api/datasets/:persistentId/?persistentId=doi%3A10.6141%2FTW-SR… ↗
metadata API · from Harvard Dataverse
Topics
- Inferred from text
- Transportation, logistics and supply chains 72%
Provenance · 1 source records, 6 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Harvard Dataverse | doi:10.6141/TW-SRDA-E93046-1 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:3509 | enrichment · Harvard Dataverse | taxonomy-embedding@1.1.0 | title+keywords+description (72%) |
| created_date | source · Harvard Dataverse | connector:dataverse@1.0.0 | |
| description | source · Harvard Dataverse | connector:dataverse@1.0.0 | /description |
| publication_date | source · Harvard Dataverse | connector:dataverse@1.0.0 | |
| title | source · Harvard Dataverse | connector:dataverse@1.0.0 | /name |
| updated_date | source · Harvard Dataverse | connector:dataverse@1.0.0 |