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

Abstract

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.

Which High-Speed Rail? LARG Approach between Plan and Design

“…The change is in the specific modal attributes that have a higher utility for rail services than for air services, representing the quality of the service offered by HSR trains. This result confirms what has been achieved in other countries [32,33].…”

“…A significant impact on the GDP of the southern regions, as previously seen from studies carried out in the areas served by the HSR, emerges experimentally within economically homogeneous areas in which there has been an increase in GDP by 7-8% in 10 years compared to areas where there is no HSR at 300 km/h; • An impact on the internal accessibility of South Italy, with the duration of most travels between Reggio Calabria and the regional capitals, and among many other connections between provincial capitals being less than 3 h; and • A significant impact on the main components of demand [30,33,56]:…”

Study of Public Transport Development in South Bali Districts: Potential Public Transport Mode

“…A study [8] utilized the stated preference method to estimate the passenger demand of a high-speed railway connecting Jakarta and Surabaya City. The estimate was based on three scenarios, namely pessimist, moderate and optimistic scenario.…”

Utilizing an Intervention Forecasting Approach to Improve Reefer Container Demand Forecasting Accuracy: A Case Study in Indonesia

“…While qualitative forecasting is only used when the amount of historical data is limited, quantitative forecasting is more commonly used among practitioners. The types of most commonly used quantitative forecasting are time series, regression model (Taylor and Letham, 2018), Autoregressive Integrated Moving Average (ARIMA) (Min, 2008;Jaipuria and Mahapatra, 2014;Dhini, 2015), Seasonal Autoregressive Integrated Moving Average (SARIMA) (Farhan and Ong, 2018;Mo et al, 2018), Artificial Neural Network (ANN) (Jaipuria and Mahapatra, 2014;Dhini et al, 2015); and Multinomial Logit Model (MNL) (Lubis et al, 2019). In circumstances of promotion or irrational events, combining qualitative and quantitative methods could be implemented to increase forecast accuracy (Min, 2008;Jaipuria and Mahapatra, 2014;Khamphinit and Ongkunaruk, 2016;Chong et al, 2017).…”