Allowance allocation and adjustment of factors affecting railway logistics demand

Because of the extra dimensions and weight carried on cargo trucks on Java’s North Coast Road (Pantura), the allowance policy was designed to give a discount on freight transportation services in exchange for government funding to pay the discount. It is relevant to supply and demand. The demand for services can be managed using this policy. The purpose of this research is to go further into the policy’s analysis. The endeavor to manage demand for logistics trucks and trains will be examined in greater depth by developing a regression model with two response variables, namely truck and train logistics transportation demand. Logistics train tariffs, the number of factories in one region, the distance between the industrial center and the nearest Indonesian Railway Logistics Company (KALOG), truck driver wages for one delivery, truck maintenance and operational costs, and the distance from the city of origin to the national capital are all expected to influence demand for logistics trucks and trains. While the Tangerang region has the highest tariff per kilometer and is also the longest distance from the industry center to KALOG, Jakarta has the highest truck operations and maintenance expenses. According to the findings of this study, logistics train prices, the distance between the industrial center and the nearest KALOG, and truck operational expenses all have a major influence on truck and train logistics transportation demand. These major elements have been shown to assist enhance KALOG’s profit while reducing overdimensioning and overloading on Pantura.


Introduction
Logistics is critical in Indonesia, particularly logistics, which is a source of revenue for the government and plays a crucial role in achieving economic and logistical success.However, in Indonesia, the logistics hub is located in Jakarta, the country's capital.Finally, this explains Jakarta's importance as a logistical hub.When the center makes a logistics policy, it affects the entire country of Indonesia.[1].As a result, it is not surprising that the present Indonesian administration, led by Mr. Joko Widodo, emphasizes infrastructure for seamless people mobility and logistics [2].However, it cannot be denied that land logistics vehicles, particularly cargo trucks, frequently suffer from ODOL (overdimension and overloading), which refers to the extra dimensions and weight carried on a cargo truck [3].
In these circumstances, initiatives to minimize ODOL are required, such as the mode change from trucks to logistics trains, which raises the cost of operating cargo vehicles for each trip.Researchers want this strategy to be enforced so that transportation, particularly the pintura lane, may be avoided and the amount of truckloads travelling via the pintura lane is reduced.Provincial economic growth in Indonesia is driven by existing infrastructure, which means that switching modes to rail will also benefit the economy by improving existing infrastructure [4].Based on Figure 1, it is known that the volume of commodities moved by trains has tended to remain constant over the last three years, implying that logistics trains have not witnessed an increase in demand [5].This is what motivates academics to develop strategies that benefit both parties, such as limiting damage to the Pintura lane as a result of the rail conversion while raising demand for logistic trains.According to Figure 1, the number of goods carried by trains has tended to remain steady over the previous three years, showing that demand for logistics trains has not increased [5].This is what drives academics to devise ways that benefit both sides, such as minimizing damage to the Pintura lane as a result of rail conversion while increasing demand for logistic trains.
There are various studies on logistics train demand, one of them is [6] that explains logistics train supply and demand.Respondents anticipate that courses covering a wide variety of activities connected to railroads and logistics, such as management, finance, training, people, and information technology, would be organized.Emphasis should also be made on commercial, operational, and management issues, which should increase rail logistics performance and productivity [6].Furthermore, colleges should use all of their resources to offer particular short courses to enable rail logistics professionals to obtain lifelong knowledge.Railways have more network complexity, pricing differentials, booking lead periods, and market competitiveness.Examples of capacity allocation demonstrate the importance of demand forecasts in providing exact projections of overall demand and demand distribution across ticket classes.However, because the research employed hypothetical data, the results of the exhibited scenarios cannot be generalized [7].As a result, more research utilizing actual demand data should be conducted.The approach also ignores the issues of limited data and the network effect.
Road freight emissions increased by around 25%, while total volume increased by 65%.In other words, Figure 2 implied that the rail industry was able to achieve a 60% absolute reduction in carbon emissions, compared to only 25% for road freight.Because zero-emission heavy vehicles will not be extensively employed until the end of this decade, transferring more freight to railroads will be the most efficient way to reduce carbon emissions in the EU transportation industry until 2030 [8].To reduce carbon emissions as fast as possible in the transportation sector, the alternative is the rail industry.KALOG (Indonesian Railway Logistics Company) is one of the freight forwarders that offers rail delivery.KALOG is a subsidiary of KAI (Indonesian Railways Company) that provides train-based logistics distribution services.KALOG's job and duty is to add value along the value chain of logistics distribution services, transportation of products, and warehousing.This concentration and strengthening of KALOG's key role may be seen in the pre-service and post-service stages of KAI's services.When compared to other motorized urban transportation modes such as private vehicles, urban rail systems such as metro and light rail may emit significantly less pollution.Rail emissions per passenger kilometer are now around one-sixth of those of air transport on a "well-to-wheels" (wing/wake) basis.The emissions from electrified passenger train are even lower when powered by nuclear or renewable energy.Significant expenditures in the rail sector are planned by European players in order to make train travel more appealing to travelers, particularly over short-haul aircraft.Expanding and exploiting rail networks will be critical to reducing emissions and moving toward the Net Zero Scenario [9].

Methods
The regression analysis approach was used in this study to determine the demand for logistic trains and trucks.The characteristics that are assumed to determine the demand for logistic trains and trucks include KALOG's tariffs, the number of industries in one region, and the distance between the industrial hub and the nearest KALOG as explained in Table 1.Meanwhile, one-time driver salaries, maintenance and operational expenses, and the distance from the city of origin to the country's capital (in this case, D.K.I Jakarta) are considered to impact truck demand.Tangerang, Jakarta, Bandung, Semarang, Yogyakarta, and Surabaya are among the provincial capitals that will be studied.These cities were selected because they have a large number of businesses that require logistics and transportation.Additional sources from the firm and KALOG in the research region were used to collect data, including information about the company's vehicle ownership.The regression model will then be improved by integrating subsidies on railway tickets as well as additional expenses on trucks, resulting in optimized results from the distribution of to-be-determined subsidies and additional charges on the usage of trucks as logistical transportation.

Variables
Description Source Logistic train demand (y1) Total goods transported by logistics train this year (in tons) [4] Truck demand (y2) Quantity of goods transported by trucks this year (in tons) [10] KALOG tariffs (x1) Price per-km + administration fee (in IDR) [11] Number of factories in a region (x2) Number of factories at the origin and destination of the train. [12] Distance KALOG (x3) Distance between KALOG and Industry center (in km) [13] Operational (x4) Operational cost [14] Maintenance (x5) Truck maintenance cost [15] Distance to capital (x6) Distance between home city and capital city [16]

Results and Discussions
Regression analysis will be performed to find the coefficient of each variable employed, which will be applied in order to create a model to calculate the ideal subsidy.The following table shows the demand for trains and trucks.According to Figure 3 and 4, the Semarang region has the highest train demand.However, the truck method is most prevalent in the Jakarta area.Trucks have significantly higher demand than rail, based on demand in each city.As a result of the increased demand for trucks to make deliveries, and because there is no additional expense, the Pantura road is frequently damaged.Researchers seek to transfer the mode from trucks to trains, which have significantly higher demand, so that demand for both modes might be balanced.The following are the KALOG rates and the distance to the nearest KALOG.Based on the results of the train and truck demand regression models shown in Table 2, the KALOG's tariff variable and the distance from the industry center to the nearest KALOG are recognized to be crucial to truck demand in the rail regression model.The coefficient for the KALOG tariff is negative, indicating that the higher the KALOG tariff, the lower the demand for KALOG.Meanwhile, the further the industry center is from KALOG, the higher the demand.It is well known that in the truck regression model, operational variables are relevant to truck demand.The coefficient for operating tariffs is negative, indicating that the higher the operational cost, the lower the demand for trucks.3. The shift in demand after receiving an allowance is seen below.The subsidy allocation for each railroad for each estimated rail market share will be known after collecting KALOG's demand growth for each expected market share.In According to Table 3, if KALOG expects a 1% market share, an average subsidy of 0.16% of the cost of a single trip is necessary.If the predicted market share is 2%, a subsidy of 0.44% is required on average.

Conclusion
The Pantura road is frequently damaged owing to the strong demand for trucks to make deliveries, and they prefer to utilize the Pantura route because it is free of charge.With the KALOG tariff and truck operation coefficients calculated, the demand for trains (by subsidizing) and trucks (by increasing operating costs) fluctuates for each regression model.Increasing the rail market share from 1% to 5% will necessitate an increase in demand from 147.75% to 1138.76%.The average subsidy for each KALOG line ranges from 0.16% to 1.26%.KAI Logistics' revenue in 2021 was 998.708 million [17], implying that the subsidy expenses varied between IDR 1,629,100,660.00 to IDR 12,555,877,980.00.
According to the Ministry of Public Works and Public Housing (PUPR), a budget allocation of IDR 1.3 Trillion [18] was disbursed in 2023 for the upgrading of the coastal route, therefore the benefits varied from IDR 7,784,230,023.00 to IDR 59,994,968,240.00with the use of subsidies.Because the profit is significantly greater than the subsidy that must be granted, the subsidy policy helps not only KALOG, but also the government by reducing the budget for restoring the coastal road.

Figure 1 .
Figure 1.Number of goods by railway transportation in Java (Thousand Tons).

Figure 2 .
Figure 2. GHG intensity of motorized passenger transport modes at the wheel (wake/wing).

Table 2 .
Coefficient of each variable used

Table 3 .
Market share adjustment of KALOG and truck.