Demand Forecast of Jakarta-Surabaya High Speed Rail based on Stated Preference Method
Corresponding email: halubis@si.itb.ac.id
Published at : 25 Apr 2019
Volume : IJtech
Vol 10, No 2 (2019)
DOI : https://doi.org/10.14716/ijtech.v10i2.2442
Lubis, H.A., Pantas, V.B., Farda, M., 2019. Demand Forecast of Jakarta-Surabaya High Speed Rail based on Stated Preference Method. International Journal of Technology. Volume 10(2), pp. 405-416
| Harun al-Rasyid Lubis | -Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40135, Indonesia -National Center for Sustainable Transportation Technology, ITB |
| Vinsensius Budiman Pantas | Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40135, Indonesia |
| Muhammad Farda | -Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40135, Indonesia -National Center for Sustainable Transportation Technology, ITB |
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.
Demand forecast; High-speed rail; Multinomial logit; Stated preference
Java Island is the busiest island in Indonesia, with Jakarta and Surabaya being the most populated cities, located at either end of the island. As economic center hubs, both Jakarta and Surabaya have very high populations, namely 10.17 million (Jakarta City Statistics Agency, 2016) and 2.85 million (Surabaya City Statistics Agency, 2016) respectively. Movement or trips between the two cities are mainly motivated by business and are currently facilitated by train, plane and road transport, including toll roads. According to the Indonesia National Railway Master Plan, the government is planning to develop a Jakarta-Surabaya high-speed train, with a length of approximately 730 km. This train will connect several cities between Jakarta and Surabaya, such as Cirebon and Semarang along the north corridor, as well as Bandung and Yogyakarta in the south. It is expected to deliver benefits in terms of energy and shorter travel times. However, in order to achieve the optimum benefits, there should be a significant modal shift, from private vehicles, planes and existing trains to the proposed high-speed train.
The development of a high-speed train as an alternative to air travel is an urgent matter. According to flightradar24 (2018), there are 591 flights weekly from Surabaya to Jakarta alone. If this figure is added to other flights along the Jakarta-Surabaya-Bali route, there are 4,000 flights monthly, or 1,000 flights weekly. This is the busiest air traffic route in Indonesia, on which airlines and users of air transportation often experience excessive delays during departure and landing. Furthermore, most airports on Java Island are over capacity by a factor of 5?7; some runways and terminals are now undergoing expansion, and a few new airports are also being built. The Southern Java air traffic navigation service is now also being considered for development, linking Jakarta, Bandung, Tasikmalaya, Purbalingga, New Yogyakarta International Airport in Kulonprogo, and Surabaya. The provision of HSR services will ultimately change the pattern of intercity travel in the corridor, as HSR will absorb some of the long-distance travelers.
The main objective of this study is to review the previous Jakarta –Surabaya HSR demand forecast, develop a new choice of mode model, and update the forecast. In this study, access and egress to and from HSR stations are represented explicitly by distance and cost parameters, which complement previous studies such as Barus et al. (2016), who found that access to stations was very important to improve the competitiveness of inter-city transportation, although it was still qualitative.
The results of the analysis of the high-speed train mode choice give insights into important attributes, which may be useful for the development of the service. Based on the MNL III model, the variable with the greatest influence is travel time, with the coefficient of -0.717, followed by mode frequency and tariff, with coefficients of -0.098 and -0.00808, respectively. Among the accessibility variables, distance has the greatest influence, with a coefficient value of (minus) 0.026, while cost has a coefficient value of (minus) 0.00377. These results show that in order for the high-speed train to attract more passengers, emphasis should be placed on travel time. In addition, HST stations must be highly accessible. This can be implemented by locating the HST stations around the city center, and by providing connecting feeder transport to and from them.
From the respondent characteristics, those that have a monthly income above IDR 5,000,000 are more likely to choose planes over high-speed trains. This is also the case for passengers with a monthly income above IDR 3,000,000. However, passengers that travel more than once a month would tend to prefer high-speed trains over planes.
Based on the MNL IIIa model, the choice probabilities for high-speed train, plane and executive train for the moderate attribute value are 33.28%, 50.36% and 16.36%, respectively. In addition, by using the MNL IIIa model, the number of passengers per day for the high-speed trains by 2050 is estimated to be 12,597 (4,597,905 passengers per year) in the moderate scenario. In the optimistic and pessimistic scenarios, the number of passengers per day is estimated to be 33,421 (12,198,665 passengers per year) and 5,376 (1,962,240 passengers per year), respectively.
Future studies should consider the transit network assignment model to reproduce ridership estimates throughout the HSR lines and nodes (stations) to verify the importance of various combination station locations in improving HSR ridership. In addition, there is a potential to develop an HSR line on the southern coast. Further studies could develop a network model for alternative HSR routes, one of which could be the combination of the southern and northern routes, starting from Jakarta to Bandung, then traversing to Cirebon on the southern coast to reach Surabaya along the northern cost corridor. Depending on the network and additional data availability, the demand for these alternative routes could be estimated in the future.
This study was made possible with the help of Angkasa Pura Corp. and Indonesia Railway Corp. as data providers. The grant from the Ministry of Research, Technology and Higher Education through “Hibah Berbasis Kompetensi (HIKOM)”, Grant Number: 127/SP2H/ PTNBH/ DRPM/ 2018 is also acknowledged in supporting this study.
| Filename | Description |
|---|---|
| R3-CVE-2442-20190304093109.png | Figure 1 - High-Speed Train Alternative Route |
| R3-CVE-2442-20190304093144.PNG | Figure 2 - Respondents Mode Choice Based on Stated Preference |
| R3-CVE-2442-20190304093206.PNG | Figure 3 - High-Speed Rail Demand Forecast |
Acharya, S., Morichi, S.J., 2013. Introducing High Speed Train (HSR) System in Developing Asia - Issues and Prospects. In: Proceedings of the Eastern Asia Society for Transportation Studies, Volume 9
Barus, L.S., Martell-Flores, H., Hadiwardoyo, S.P., Batoz, J., 2016. Intercity Mode Choice Modelling: Considering the Intracity Transport Systems with Application to the Jakarta-Bandung Corridor. International Journal of Technology, Volume 7(4), pp. 581–591
Berawi, M.A., Berawi, A.R.B, Prajitno, I.S., Nahry, N., Miraj, P., Abdurachman, Y., Tobing, E., Ivan, A., 2015. Developing Conceptual Design of High Speed Railways using Value Engineering Method: Creating Optimum Project Benefits. International Journal of Technology, Volume 6(4), pp. 670–679
Flightradar24., 2018. Surabaya Juanda International Airport. Available Online at https://www.flightradar24.com/data/airports/sub, Accessed on June 27, 2018
Greene, W.H., 2011. LIMDEP Student References Guide - Econometric Software. Version 9. New York: Econometric Software, Inc
Indonesia Railway Corp (PT. KAI), 2015. Annual Report. Bandung. PT. KAI
Jakarta City Statistics Agency., 2016. Jakarta City in Figures. Jakarta: Jakarta City Statistics Agency
JICA, 2012. Study on High Speed Railway Project (Jakarta – Bandung Section) Final Report. Jakarta: Japan International Consultants for Transportation
Koppelman, F.S., Bhat, C., 2006. A Self Instructing Course in Mode Choice Modeling: Multinomial Logit and Nested Logit Models. U.S Department of Transportation
Latief, Y., Berawi, M.A, Rarasati, A.D, Supriadi, L.S., Berawi, A.R.B., Hayuningtiyas, I.S., 2016. Mapping Priorities for the Development of the Transportation Infrastructure in the Provincial Capitals of Indonesia. International Journal of Technology, Volume 7(4), pp. 544–552
PT LAPI ITB, 2015. Data Renewal and Demand Forecast Update – High Speed Railway (Jakarta – Bandung Section) Final Report. Bandung: LAPI ITB
PT LAPI ITB, 2016. Surabaya - Semarang High Speed Rail (Demand Forecast) Final Report. Bandung: LAPI ITB
National Statistics Agency (BPS), 2010. Population Census 2010. Available Online at: https://sp2010.bps.go.id/index.php/navigation/wilayah, Accessed on November 16, 2016
National Statistics Agency (BPS), 2016. Number of Railway Passenger. Available Online at https://www.bps.go.id/linkTabelStatis/view/id/1417, Accessed on 16 November 2016
Ortuzar, J.D., Willumsen, L.G., 2011. Modelling Transport. 4th Edition. Chichester, United Kingdom: John Wiley & Sons Ltd
Sanko, N., 2001. Guidelines for Stated Preference Experiment Design. Master Theses, Graduate Program, Ecole Nationale des Ponts et Chaussees, Paris
Shukuri, M., 2014. Exploring the Successful Stories of Shinkansen – High Speed Rail based on Crash Avoidance Principles. International High Speed Rail Association
Surabaya City Statistics Agency, 2016. Surabaya City in Figures (Surabaya Dalam Angka). Surabaya: Surabaya City Statistics Agency
TEMS (Transportation Economics & Management Systems), 2008. High Speed Rail Feasibility Study Methodology Technical Report. United States: TEMS, Inc/Quandel Consultants, LLC