International Journal of Technology (Apr 2019)

Demand Forecast of Jakarta-Surabaya High Speed Rail based on Stated Preference Method

  • Harun al-Rasyid Lubis,
  • Vinsensius Budiman Pantas,
  • Muhammad Farda

DOI
https://doi.org/10.14716/ijtech.v10i2.2442
Journal volume & issue
Vol. 10, no. 2
pp. 405 – 416

Abstract

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Intercity roads, rail networks and air transport in Java, Indonesia, have suffered greatly due to the congestion of goods and passenger transport.  The plan to build a 730 kilometer high-speed rail (HSR) route from Jakarta, the state capital, to Surabaya, the capital of East Java Province, has been discussed in the public sphere for years. The Government of Indonesia (GOI) plans to connect these two cities by HSR to supplement the alternatives, such as conventional rail, air and toll roads. The HSR service is expected to reduce the existing average intercity train travel time from nine hours to five hours, or even to three depending on the maximum design speed. Currently, door-to-door air travel may take five hours. Another goal of the Jakarta-Surabaya HSR is to improve accessibility between major cities in Java, reduce congestion between them, and reduce air pollution, accidents and energy consumption along the transport corridor. The purpose of this study is to estimate the number of passengers from existing modes of transportation (e.g. road, rail and air) who would be willing to change their choice of mode to the planned high-speed trains. The data for the study are based on stated choice questions posed to respondents, in which the differences in attributes such as travel time and cost; service frequency or headway; and accessibility, such as the distance and cost to reach the stations, are the main factors influencing switching behavior to the new HSR services. The chosen model is the MNL III model, with 45.36% accuracy and 0.128 pseudo R-square. By using the Multinomial Logit model (MNL), the study reveals that the most important variable is travel time, followed by frequency and cost. The MNL model is also used to estimate the initial HSR ridership to produce the demand forecast along the planning horizon.

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