Enhancing Passenger Volume for the Jakarta-Bandung High-Speed Rail: A System Dynamics Approach to Sustainable Regional Development

HST represents an important development in Indonesia since the initiative significantly reduces travel time, stimulates regional economic activity, enhances inter-connectivity, and supports the infrastructure-focused strategy outlined in the National Medium-Term Development Plan (RPJMN) 2020–2024, as formalized through Presidential Regulation No. 18 of 2020.

An alternative examination shows that the high-speed train project in Indonesia possesses the capacity to markedly enhance transportation efficiency, catalyze economic expansion, and promote urban development, contingent upon the effective application of the insights garnered from other nations [4].

A significant area of inquiry has investigated the influence of HST on economic advancement, obtaining critical insights into the factors that shape passenger preferences, methodologies for stimulating demand, and the extensive repercussions on regional progress. These investigations emphasize the significance of dynamic systems in enhancing HST operations. The methodologies include surveys, statistical analysis, econometric modeling, case research, comparative analysis, system dynamics modeling, and simulations. Collectively, the strategies provide a holistic comprehension of the elements contributing to the success of HST.

Economic growth and development within Indonesia can be improved by enhancing market accessibility for enterprises located in Jakarta, Karawang, and Bandung, potentially elevating collective income by 12-18%. This may contribute to a 0.4% increase in real GDP, particularly during the construction phase. However, the initiative also includes certain risks, including potential job losses in traditional industries and complications associated with land acquisition, which can adversely influence overarching success and ramifications for urban development [5].

The demand for HST passengers traveling from Jakarta to Bandung is anticipated based on the growth of GDP per capita, the expansion of the manufacturing sector, and the real estate market. The progression of regional macroeconomic indicators is largely consistent with the national averages. The economic growth scenario for Jakarta will be extrapolated using the national GDP growth rate to assess HST passenger demand.

All critical parameters essential for the operational success must be harmonized with the established frameworks to attain the anticipated economic growth objectives. An important parameter is the volume of passengers scrutinized to ascertain the level of expectations. This research investigates methods to augment passenger volume and assesses the implications for regional development, particularly concerning land utilization.

Research contribution

The Jakarta–Bandung HST, spanning 143 kilometers, marks a significant milestone in the transportation infrastructure, representing the first operational HST system within the inaugural year of service. Even though the corridor is relatively short by global standards.

HST systems show optimal performance at distances of approximately 150 kilometers, below which road-based modes such as private cars and intercity buses tend to remain more competitive. This threshold is influenced by factors such as station location, access time, and the total door-to-door travel experience. According to empirical results in China, HST services attract significant modal shifts and generate new passenger demand when distances exceed 150 km, particularly on routes connecting medium-to-large cities. The Beijing–Tianjin and Tianjin–Jinan corridors provide clear examples of successful HST operation within the minimum distance benchmark [6]. These distance ranges effectively enhance time-space compression, a key factor in boosting economic growth. Consequently, the influence on passenger numbers is assessed to ascertain whether it fulfills the established targets, as well as to analyze its ramifications on regional development, particularly concerning alterations in land utilization and the capacity of land to sustain such changes.

Dynamic system analysis aims to provide a comprehensive understanding of the effect of HST on passenger volumes over time, with detailed insights into land use changes related to accommodations and resource allocation. This analysis seeks to develop recommendations for maximizing the benefits of HST development through the integration of land use and transportation planning, supported by forecasts and scenario analyses to assist urban planning and policymaking. Additionally, the conceptual causal cycle of system dynamics model, which centres on HST transportation between Jakarta and Bandung, shows substantial impacts on hotel occupancy rates in Bandung. Several key objectives are outlined for using dynamic system method, including: (a) simulating the volume of Jakarta-Bandung HST passengers generated from demographic trends, (b) executing diverse scenarios to enhance the number of Jakarta-Bandung HST passengers, (c) modelling the residual carrying capacity of the Jakarta-Bandung HST system, and (d) simulating the remaining land capacity designated for accommodation purposes. This dynamic interaction is visualized in the Causal Loop Diagram (CLD). Figure 2 shows the limitation posed by Jakarta-Bandung HST capacity (Loop B1) and the constraint from limited land availability for accommodations (Loop B2).

1. Loop B1: Population growth leads to an increase in the number of railway users. A portion of these railway users will choose to use the Jakarta-Bandung HST. As the number of Jakarta-Bandung HST users increases, the remaining HST capacity will decrease to a maximum capacity of 30,000–60,000 passengers per day. Meanwhile, the current realized number is around 10,000–20,000 passengers per day.
2. Loop B2: Population growth will lead to an increase in the number of passengers. These passengers require accommodation services, such as lodging and other related services, which require land. The available land is around 4,000 hectares, with 5% allocated for accommodation purposes.

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Figure 2. Causal Loop Diagram (CLD)

4.1 Model implementation

Model implementation includes simulating monthly data from January 2024 to January 2034, with the period from January 2024 to December 2025 serving as the baseline for calibrating projections. The population projections utilized in this study are derived from comprehensive regional statistical data for the Jakarta Metropolitan Area (Jabodetabek), as presented in Table 1, and for Bandung and its surrounding regions, as detailed in Table 2. Regional population growth is a primary driver for passenger volume forecasts, stating the influence of urban dynamics on transportation needs. Simultaneously, HST passenger demand is informed by operational records from the Jakarta–Bandung Whoosh service. Travel patterns, such as annual passenger movements between Jakarta and Bandung, are sourced from the Ministry of Transportation’s Origin-Destination (OD) survey, providing a critical basis for understanding baseline travel behaviors and future projections, as presented in Table 3.

1. Jakarta and the surrounding areas: Includes all cities/regencies in DKI Jakarta, Bogor City and Regency, Depok City, Tangerang City and Regency, and South Tangerang City.
2. Bandung and the surrounding areas: Includes Bandung Regency, West Bandung Regency, Bandung City, and Cimahi City.

Table 1. Population data for Jakarta and surrounding areas (in thousands)

Source: Statistical Data of Indonesia 2024

Table 2. The population data of Bandung and its surrounding areas (in thousands)

Source: Statistical Data of Indonesia 2024

Table 3. Travel patterns between Jakarta and Bandung

Source: Ministry of Transportation

Table 4. The passenger growth scenario settings

Notes: Sce0: Business-as-usual condition, with all parameters set to zero. Sce1: Passenger growth stimulated by increasing public holidays. Sce2: Implementation of a time-based ticket pricing system, fuel subsidies, and promotion of tourism events. Sce3: Enhancement of feeder systems in Jakarta and Bandung. Sce4: Comprehensive combination of all interventions.

4.2 The scenario

Several policy intervention scenarios have been developed to enhance passenger growth forecasts. These scenarios focus on levers such as promoting tourism events, introducing time-based ticket pricing, adjusting fuel subsidies, and strengthening feeder transportation connectivity to and from HST stations. Scenario evaluations were informed through consultations with experts in transportation, regional development, tourism, and public policy. These expert inputs were utilized to assign optimal and logically consistent scores to each scenario used in the system dynamics simulation, as detailed in Table 4. Each intervention is expected to influence travel behaviour differently, and the combined impacts are explored through dynamic scenario simulations.

4.3 Stock flow diagram

In the context of system dynamics analysis, the model is divided into population, passenger demand, passenger volume metrics, policy scenarios, and land availability. The population assumption is based on five key cities and the surrounding areas of Jakarta and Bandung considered representative of the region. HST passenger data is sourced from monthly records of HST Jakarta-Bandung, covering the period from January 2024 to January 2025.

The interrelationships among the variables shown in the CLD are translated into a system dynamics program, as shown in Figure 3.

Simulation of policy scenarios enables urban planners and policymakers to assess the potential effectiveness of different interventions and combinations. This method allows for dynamic evaluation of policy changes on passenger volumes and operational sustainability.

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Figure 3. Policy scenario illustration

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Figure 4. Land availability forecast

Land availability, a critical constraint supporting the growth of accommodation services related to increased HST passengers, is also modelled. Land allocation projections of ±4,000 hectares with an estimated 5% allocated for accommodation are visualized in Figure 4, emphasizing the spatial limitations considered in urban development plans. These integrated components establish a robust system dynamics framework, enabling the forecasting of future impacts of HST on passenger volumes and land use. This comprehensive model provides evidence-based support for more effective infrastructure planning, urban policy design, and sustainable regional development.

Together, these integrated components establish a robust system dynamics framework, enabling the forecasting of future impacts of HST on passenger volumes and land use. This comprehensive model provides evidence-based support for more effective infrastructure planning, urban policy design, and sustainable regional development.

A system dynamics simulation shows that passenger growth on the Jakarta-Bandung HST corridor peaks in December, primarily due to holiday-related travel. However, from February to April, ridership remains low, posing challenges to the government’s operational and financial targets. To address fluctuations and balance with long-term development goals, strategic interventions such as service improvements, seasonal pricing, and enhanced feeder connectivity are essential.

Recent modeling efforts show that time-sensitive pricing, formulated through a bi-level optimization paradigm, facilitates a dual objective, namely the maximization of operator revenue alongside the minimization and equalization of passenger costs, redistributing demand from peak to off-peak time-frames and enhancing overall system efficiency [10]. Travel behavior research comparing HST and road passenger transport (RPT) in selected Chinese cities reported that passengers prioritized speed, comfort, and punctuality when choosing HST. Meanwhile, fare affordability and safety were more influential for RPT users. Using a logit model, the analysis shows that enhancing RPT’s comfort, safety, and fare structure can significantly raise the attractiveness, with income levels and regional preferences playing a crucial role in shaping travel mode decisions [11].

Empirical evidence based on difference-in-differences estimation supports the assertion that HST development stimulates tourism, specifically in nodal cities integrated within national networks, increasing visitor flows and local revenues [12, 13]. A comparative research confirms that passengers prefer HST over air travel when service attributes such as the speed between HST and air travel and consumer behavior are emphasized [14]. In this context, tiered and adaptive pricing structures, combined with strategic seat allocation models, have proven effective in strengthening financial viability and contributing to potential revenue [15, 16].

This research shows that enhancing multimodal accessibility, particularly by increasing the availability of feeder services such as buses, subways, and regional railways, has a more significant impact on HST ridership than reducing transfer times or improving service frequency. The result emphasizes that the quantity of supporting facilities around HST stations plays a critical role in improving first- and last-mile connectivity, promoting greater use of HST services [17]. The research on China’s Beijing–Shanghai corridor and Japan’s Shinkansen network report that managing peak-period demand in HST systems requires more than speed enhancements. Reliable, cost-effective, and well-integrated transport particularly through frequent short-distance services and seamless last-mile connectivity proves more effective in increasing ridership and sustaining tourism flows during high-demand periods. Integrated feeder systems and local accessibility are essential to optimize the benefits of HST during peak time [18, 19]. Additionally, accessibility research shows that access travel time to HST stations strongly shapes network utility and perceived attractiveness [20]. Based on the results, this research outlines two strategic government policies aimed at maximizing HST operations and improving regional development to manage passenger demand and prepare land for future needs.

6.1 Passenger demand management

Building on the preceding system dynamics simulation and empirical results, the effectiveness of Jakarta–Bandung HST operations depends significantly on the management of passenger demand. Addressing seasonal fluctuations, improving station accessibility, and balancing service offerings with user preferences are critical to sustaining ridership growth. This section outlines five interrelated strategies that form a coherent policy approach by improving feeder connectivity, implementing flexible pricing, rationalizing fuel subsidies, responding to temporal travel patterns, and balancing land use with transit infrastructure.

6.1.1 Enhancement of feeder services

A key determinant of HST ridership is the quality of feeder access. The use of peripheral stations can lower infrastructure costs and improve scheduling flexibility but creates challenges in accessibility. Lengthy and costly access or egress from city centers may offset the benefits of low fares and deter potential users. Strengthening feeder connections between HST stations and urban transport networks is essential to enhance convenience, increase ridership, and ensure the overall effectiveness of low-cost services, particularly for price-sensitive passengers and families [18, 21]. These patterns reinforce the importance of feeder integration, particularly for promoting non-commuter and tourism-related travel.

6.1.2 Ticket pricing based on time and tourism events

A review of China’s HST pricing practices confirms the effectiveness of dynamic and time-sensitive pricing strategies in improving revenue and capacity utilization. By integrating dynamic pricing with seat allocation, this research shows that adjusting fares based on demand elasticity, travel time, and seasonal variations such as holidays or peak travel periods can increase revenue by 4.95% compared to fixed pricing. The results report that pricing strategy is as critical as physical connectivity in shaping traveler behavior, optimizing seat occupancy, and achieving operational efficiency [22]. Within the Beijing-Shanghai corridor in China, operators have shifted from static to dynamic fare structures adjusted in line with seasonal variations, departure times, and service classes [23]. These methodologies show the advantages of adaptive pricing, particularly in contexts characterized by high demand.

Pricing strategies ought to be informed by traveler segmentation when applied to the Jakarta-Bandung corridor. Variations in travel behavior and willingness to pay are evident across user groups. Business travelers prioritize time savings and seamless connectivity, while leisure passengers are more responsive to fare levels. These differences allow differentiated pricing models to manage demand and maximize revenue potential [24]. Research emphasizes the role of inter-urban train in improving suburban economic development, which requires effective management of environmental capacity. The 2024 West Java Province Tourism Market Analysis shows that 53%, 23%, and 24% of Whoosh passengers travel for leisure, business, and commuting or education, respectively. Even though 63.7%, 57.9%, and 66% of domestic travelers opt for private vehicles, trains, and HST, focusing on affordability and efficient pricing options can facilitate a more uniform distribution of demand across the week. Pricing evolves into a proactive mechanism for demand management and congestion alleviation when integrated with strategic marketing initiatives surrounding tourism events. Similarly, urban areas connected to HST network have experienced a significant increase in tourism activity, with revenue increasing by 22% and tourist arrivals rising by 38% compared to centers without HST access [25].

6.1.3 Fiscal incentives and fuel subsidy reduction

To further promote a modal shift towards HST travel, the consideration of fiscal disincentives aimed at reducing private vehicle usage through the minimization of fuel subsidies is warranted. In addition to the environmental advantages, such policies re-calibrate cost-benefit analyses in favor of mass transit systems. According to Banister [26], fiscal instruments play a crucial role in facilitating inter-modal integration. Complementary incentives, including fare reductions for off-peak HST services can enhance utilization rates and preserve affordability for flexible traveler demographics. The effectiveness of policies is maximized when executed to create a synergistic effect through behavioral nudges and pricing efficiency.

6.1.4 Weekend sensitivity

Seasonal and temporal variations in ridership necessitate strategic management to optimize service delivery. Passenger demand peaks on weekends, primarily due to leisure travel and extended holidays. Furthermore, government initiatives and major events, such as collective leave days and international concerts, enhance ridership. Simulations predict a 5% rise in travel due to holidays and a 30% increase from events in Jakarta and Bandung, which significantly impacts HST demands.

6.1.5 The impact of land use planning

Beyond immediate operational considerations, the long-term viability of HST systems depends on strategic land use planning in proximity to stations. HST infrastructure exerts a significant influence on urban morphology, increases commercial development, and stimulates regional tourism, particularly within the hospitality and retail sectors. Empirical research shows that municipalities served by HST experience tourist growth rates exceeding four times the areas lacking access [27]. However, these benefits are not uniformly distributed. Smaller and medium-sized cities located in central and western regions frequently realize greater gains compared to saturated urban centers, while smaller or less-developed cities may experience limited or even negative effects due to a siphon effect, necessitating context-specific planning initiatives [28].

The spillover effects of HST extend beyond directly connected nodes. Furthermore, peripheral areas indirectly connected to HST lines experience tourism-related growth, affirming the broader spatial implications of HST systems [24]. In this context, system dynamics modeling is essential to capture the complex interconnections among transportation, land use, and economic activity [29].

Despite the increasing number of passengers, assessing the operations of the Jakarta-Bandung HST over the next decade will be crucial to increase ridership. Central and regional governments need to implement strategic policies that promote mass transit use, such as enhancing connectivity, improving tourism attractions, and developing areas around stations to stimulate investment interest. The potential for HST Jakarta-Bandung to reach a target of 30,000 passengers per day during certain months, particularly around holidays and national celebrations, is promising. The capacity of HST and the available land are sufficient to accommodate passenger growth in the coming decade. However, changes in land use resulting from increased economic activity due to HST Jakarta-Bandung expansion have not yet been included in the analysis.