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Record W2414519064 · doi:10.1115/jrc2016-5778

Exploring the Opinions of Passenger Rail Riders: Evidence From the Hoosier State Train

2016· article· en· W2414519064 on OpenAlexaboutno aff
V. Dimitra Pyrialakou, Konstantina Gkritza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)Fiscal yearInvestment (military)Service (business)Order (exchange)Quarter (Canadian coin)EngineeringTransport engineeringBusinessOperations researchFinancePolitical scienceGeographyComputer scienceMarketingArchaeologyPolitics

Abstract

fetched live from OpenAlex

Ridership on Midwest passenger rail lines has been steadily increasing over the past two decades. Between 2005 and 2014, there has been a growth of more than 65 percent, much higher than the national average (approximately 30 percent for the same years). Nevertheless, a number of lines have discontinued their services or are in danger of discontinuance. For example, Kentucky Cardinal, operating between Chicago, Illinois and Louisville, Kentucky was discontinued in 2003, and the Three Rivers train, operating between Chicago, Illinois and New York, New York was discontinued in 2005. The Hoosier State train running between Indianapolis, Indiana and Chicago, Illinois would have faced the same fate recently, if not for the financial support that the state and communities have been providing since 2013. As of October 1, 2013, the State of Indiana, local communities, and Amtrak reached an agreement to support the Hoosier State line for the following fiscal year (2013–2014), and the agreement has continued ever since. In the meantime, the Indiana Department of Transportation (INDOT) was the first nationally to announce a Request for Proposals to seek competing solutions from independent providers, as allowed by the Passenger Rail Investment and Improvement Act of 2008 (PRIIA), in order to obtain private-sector competitive bids for the operation of the Hoosier State train. Recently, after many unfruitful attempts and many obstacles, INDOT reached an agreement with Iowa Pacific Holdings. The company has been providing the locomotives for the line since August 2015, and collaborates with Amtrak to keep the train in service, with a shared vision to increase service frequency, improve speed and maintain a reliable schedule, and provide better on-board amenities. However, to ensure the financial viability of the system and support any improvement or expansion, an increase in ridership is necessary. To achieve this, it is essential that we understand the opinions of Indiana residents, passengers of the Hoosier State train, and advocates of the line towards passenger rail. This paper presents the results of a survey that was conducted on board the Hoosier State train to solicit information pertaining to the perceived ease of use and usefulness of the passenger rail services, riders’ opinions, and other factors that might affect behavior toward passenger rail transportation, as well as factors that affect an individual’s mode choice in general, such as habitual automobile behavior, or external impedance factors like schedule and route restrictions. The survey was endorsed by INDOT and approved by Amtrak and Iowa Pacific Holdings. In addition, this paper presents how opinions toward passenger rail differ among different groups based on socioeconomic and demographic characteristics, familiarity with passenger rail transportation in general and the Hoosier State train specifically, and usage. Furthermore, in order to prioritize service improvements that can foster an increase in the Hoosier State ridership, this paper explores mode choice decisions through the use of a multi-attribute attitude model. The results of this paper can guide policy and planning decision making that aims to foster an increase in passenger rail ridership through a mode shift from personal automobiles and competing mass transportation systems, such as airlines and intercity buses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.144
GPT teacher head0.236
Teacher spread0.092 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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