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Original scientific paper

https://doi.org/10.7307/ptt.v32i6.3379

Estimating a New Fare for Sightseeing Trains Based on Willingness to Pay

Kwang-Kyun Lim ; Songwon University


Full text: english pdf 958 Kb

page 773-787

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Abstract

Since high-speed train operation in 2004 in Korea, the revenue of conventional trains has been rapidly declining. To overcome the circumstance even a little, sightseeing trains have been introduced along ten competitive routes since 2013, which helped to reduce the loss rate from 3.0 to 2.5 compared to the existing conventional trains. Such accomplishment was based on the existing fare system fitted to conventional trains, not reflecting the value of the unique service that only the sightseeing train provides. The understanding of the Willingness To Pay (WTP) has largely remained unexplored in the railway transportation literature, and further no contributions in the sightseeing train industry. The paper aims to estimate the economic value of various types of service for sightseeing trains in the contexture of the WTP postulates using open-ended question survey data and a Tobit censored regression with four different statistical structures. The normal distribution model replicates the WTPs best fitted over entire service types, and the WTPs vary by different type of train services such as recreational activities, slow-moving operation, seating type, tourist commentary and locally connected tour service. The highest value 13.3~24.2% in room typed seats compared to observable seats has been observed. Applying the demand elasticities to price, the revenue maximizing is observed at a 6% hike for a standard seat and a 22% hike for a designated seat, and the revenue rises by 0.33% to 3.54%. This study expects that the result can be used as an appropriate guideline in determining a new fare fitted to sightseeing trains.

Keywords

willingness to pay; sightseeing train; demand elasticity; Tobit censored regression model

Hrčak ID:

253155

URI

https://hrcak.srce.hr/253155

Publication date:

18.11.2020.

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