DOI : https://doi.org/10.18517/ijaseit.15.1.19357

Shipment Size Analysis on Cross-Border Freight Transportation Using Stereotype Logistic Regression: Case Study Between West Kalimantan, Indonesia and Sarawak, Malaysia

Said Basalim (1), Danang Parikesit (2), Sigit Priyanto (3), Muhammad Zudhy Irawan (4)
(1) Department of Civil Engineering, Faculty of Engineering, Tanjungpura University, Pontianak, Indonesia
(2) Department of Civil and Environmental, Faculty of Engineering, Gadjah Mada University, Yogyakarta, Indonesia
(3) Department of Civil and Environmental, Faculty of Engineering, Gadjah Mada University, Yogyakarta, Indonesia
(4) Department of Civil and Environmental, Faculty of Engineering, Gadjah Mada University, Yogyakarta, Indonesia
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S. Basalim, D. Parikesit, S. Priyanto, and M. Z. Irawan, “Shipment Size Analysis on Cross-Border Freight Transportation Using Stereotype Logistic Regression: Case Study Between West Kalimantan, Indonesia and Sarawak, Malaysia”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 1, pp. 180–188, Feb. 2025.
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Cross-border freight transport is vital for regional logistics, facilitating trade between neighboring countries. However, inefficiencies in shipment size decisions, such as overloading or underutilizing vehicles, can lead to increased costs and logistical challenges. This study investigates the key factors influencing shipment size decisions in the West Kalimantan (Indonesia)–Sarawak (Malaysia) cross-border corridor. Data from 2,017 truck trips conducted between 2016 and 2018, provided by the Fish Quarantine and Inspection Agency, was used for the analysis. Ordinal logistic regression methods were employed to examine the relationship between shipment size and influencing variables, including company type, commodity value, transport distance, and vehicle type. Shipment sizes were categorized into five groups, ranging from less than 500 kg to more than 7,500 kg. The results indicate that the stereotype logistic regression model better fits the data than the generalized ordered logit model based on log-likelihood, R-squared, and information criteria values. Key findings highlight that firm classification and commodity value significantly impact shipment size decisions, while transport distance and vehicle type have a less pronounced effect. This study emphasizes optimizing shipment size decisions to enhance cross-border transport efficiency and reduce costs. The findings offer valuable insights for policymakers and logistics companies to develop targeted strategies, especially in the context of marine commodity exports. The study improves regional trade competitiveness and promotes sustainable logistics practices by addressing inefficiencies in shipment size decisions.

R. Jakab, “Prohibition of cross-border water transport in the conditions of the Slovak Republic and its legal consequences,” J. Agric. Environ. Law, vol. 18, no. 35, pp. 49–63, Dec. 2023, doi: 10.21029/JAEL.2023.35.49.

H. Wei, “Optimal design of an integrated cross-border logistics network for China’s inland regions,” J. Coast. Res., vol. 37, no. 3, pp. 644–655, May 2021, doi: 10.2112/jcoastres-d-20-00051.1.

F. Ulengin et al., “A simulation-based approach for improving the largest border crossing between Europe and Turkey,” Transp. Policy, vol. 114, pp. 350–363, Dec. 2021, doi: 10.1016/j.tranpol.2021.10.011.

G. Daudin, J. Hericourt, and L. Patureau, “International transport costs: New findings from modeling additive costs,” J. Econ. Geogr., vol. 22, no. 5, pp. 989–1044, Sept. 2022, doi: 10.1093/jeg/lbac007.

Y. Wang et al., “The volume of trade-induced cross-border freight transportation has doubled and led to 1.14 gigatons CO2 emissions in 2015,” One Earth, vol. 5, no. 10, pp. 1165–1177, Oct. 2022, doi: 10.1016/j.oneear.2022.09.007.

C. Liu, J. Wu, and H. L. Jayetileke, “Overseas warehouse deployment for cross-border e-commerce in the context of the Belt and Road Initiative,” Sustainability, vol. 14, no. 15, pp. 1–16, Aug. 2022, doi: 10.3390/su14159642.

B. Dong, M. Duan, and Y. Li, “Exploration of joint optimization and visualization of inventory transportation in agricultural logistics based on ant colony algorithm,” Comput. Intell. Neurosci., vol. 2022, pp. 1–12, June 2022, doi: 10.1155/2022/2041592.

R. A. Patil, A. D. Patange, and S. S. Pardeshi, “International transportation mode selection through total logistics cost-based intelligent approach,” Logistics, vol. 7, no. 3, pp. 1–26, Sep. 2023, doi: 10.3390/logistics7030060.

C. F. Gao, Z. H. Hu, and Y. Z. Wang, “Optimizing the hub-and-spoke network with drone-based traveling salesman problem,” Drones, vol. 7, no. 1, pp. 1–25, Dec. 2023, doi: 10.3390/drones7010006.

L. Ni and X. Wang, “Load factors of less-than-truckload delivery tours: An analysis with operation data,” Transp. Res. E: Logist. Transp. Rev., vol. 150, pp. 1–18, Jun. 2021, doi: 10.1016/j.tre.2021.102296.

E. A. Thompson, R. Abudu, and S. Zheng, “Empirical analysis of multiple-criteria decision-making (MCDM) process for freight transportation mode selection,” J. Transp. Technol., vol. 12, no. 1, pp. 28–41, Jan. 2022, doi: 10.4236/jtts.121002.

Y. Lu and S. Wang, “Optimization of joint decision of transport mode and path in multi-mode freight transportation network,” Sensors, vol. 22, no. 13, pp. 1–18, Jun. 2022, doi: 10.3390/s22134887.

G. Günay, “Shipment size and vehicle choice modeling for road freight transport: A geographical perspective,” Transp. Res. A: Policy Pract., vol. 173, p. 103732, Jul. 2023, doi: 10.1016/j.tra.2023.103732.

G. Bray and D. Cebon, “Selection of vehicle size and extent of multi-drop deliveries for autonomous goods vehicles: An assessment of potential for change,” Transp. Res. E: Logist. Transp. Rev., vol. 164, p. 102806, Aug. 2022, doi: 10.1016/j.tre.2022.102806.

X. Yang, “Patterns of export shipments,” J. Econ. Stud., vol. 50, no. 2, pp. 283–299, Feb. 2023, doi: 10.1108/JES-11-2021-0575.

A. Gohari et al., “Comparative analysis of single- and multi-criteria container transport modes in Peninsular Malaysia,” Int. J. Sustain. Eng., vol. 14, no. 5, pp. 1239–1250, Jun. 2021, doi: 10.1080/19397038.2020.1774819.

BKIPM, “Cross-border goods vehicle journey data, marine commodities destined for Malaysia, 2016–2018,” Entikong, West Kalimantan, Indonesia, 2018, unpublished report.

U. M. Mbanaso, L. Abrahams, and K. C. Okafor, “Research philosophy, design and methodology,” in Research Techniques for Computer Science, Information Systems and Cybersecurity, 1st ed., Springer Nature Switzerland AG, May 2023, pp. 1–32, doi: 10.1007/978-3-031-30031-8_6.

A. K. Reda et al., “Temporal stability of shipment size decisions related to choice of truck type,” Transportmetrica A: Transp. Sci., vol. 20, no. 3, pp. 1–29, May 2023, doi: 10.1080/23249935.2023.2214635.

Y. Yang et al., “Identifying intercity freight trip ends of heavy trucks from GPS data,” Transp. Res. E: Logist. Transp. Rev., vol. 157, pp. 1–21, Jan. 2022, doi: 10.1016/j.tre.2021.102590.

U. Ahmed and M. J. Roorda, “Joint and sequential models for freight vehicle type and shipment size choice,” Transportation, pp. 1613–1629, Oct. 2023, doi: 10.1007/s11116-022-10289-6.

J. Holguín-Veras, L. Kalahasthi, S. Campbell, C. A. González-Calderón, and X. (Cara) Wang, “Freight mode choice: Results from a nationwide qualitative and quantitative research effort,” Transp. Res. A: Policy Pract., vol. 143, pp. 78–120, Jan. 2021, doi: 10.1016/j.tra.2020.11.016.

K. Backhaus, B. Erichson, S. Gensler, R. Weiber, and T. Weiber, Multivariate Analysis: An Application-Oriented Introduction, 2nd ed. Springer Gabler, pp. 279–299, Nov. 2022, doi: 10.1007/978-3-658-40411-6.

Statistical Consulting Group, “Logistic regression with STATA,” 2021. [Online]. Available: https://stats.oarc.ucla.edu/stata/seminars/stata-logistic/ (accessed Apr. 22, 2021).

J. S. Long and J. Freese, Regression Models for Categorical Dependent Variables Using Stata, 3rd ed. Texas: Stata Press, 2014, pp. 120–132.

N. M. Zafri, A. Khan, S. Jamal, and B. M. Alam, “Risk perceptions of COVID-19 transmission in different travel modes,” Transp. Res. Interdiscip. Perspect., vol. 13, pp. 1–10, Jan. 2022, doi: 10.1016/j.trip.2022.100548.

J. Romão and Y. Bi, “Determinants of collective transport mode choice and its impacts on trip satisfaction in urban tourism,” J. Transp. Geogr., vol. 94, pp. 1–9, Jun. 2021, doi: 10.1016/j.jtrangeo.2021.103094.

A. Nikitas, A. E. Vitel, and C. Cotet, “Autonomous vehicles and employment: An urban futures revolution or catastrophe?,” Cities, vol. 114, pp. 1–14, Jul. 2021, doi: 10.1016/j.cities.2021.103203.

A. B. A. Al-Mekhlafi et al., “Impact of safety culture implementation on driving performance among oil and gas tanker drivers: A partial least squares structural equation modelling (PLS-SEM) approach,” Sustainability, vol. 13, no. 16, pp. 1–17, Aug. 2021, doi: 10.3390/su13168886.

A. Allee, L. R. Lynd, and V. Vaze, “Cross-national analysis of food security drivers: Comparing results based on the Food Insecurity Experience Scale and Global Food Security Index,” Food Secur., vol. 13, no. 5, pp. 1245–1261, Mar. 2021, doi: 10.1007/s12571-021-01156-w.

P. Thaithatkul, S. Chalermpong, W. Laosinwattana, and H. Kato, “Mobility, activities, and happiness in old age: Case of the elderly in Bangkok,” Case Stud. Transp. Policy, vol. 10, no. 2, pp. 1462–1471, Jun. 2022, doi: 10.1016/j.cstp.2022.05.010.

I. E. Ceyisakar et al., “Ordinal outcome analysis improves the detection of between-hospital differences in outcome,” BMC Med. Res. Methodol., vol. 21, no. 1, pp. 1–11, Jan. 2021, doi: 10.1186/s12874-020-01185-7.

X. Liu, Applied Ordinal Logistic Regression Using Stata, 1st ed. California: Sage Publications, Inc., 2016, pp. 235–357.

M. E. Lelisho, A. A. Wogi, and S. A. Tareke, “Ordinal logistic regression analysis in determining factors associated with socioeconomic status of household in Tepi Town, Southwest Ethiopia,” Sci. World J., vol. 2022, pp. 1–9, Feb. 2022, doi: 10.1155/2022/2415692.

R. Williams, “Generalized ordered logit/partial proportional odds models for ordinal dependent variables,” Stata J., vol. 6, no. 1, pp. 58–82, 2006, doi: 10.1177/1536867X0600600104.

R. Williams, “Gologit2: A program for generalized logistic regression/partial proportional odds models for ordinal dependent variables,” Notre Dame, United States, 2005, pp. 1–18.

R. Williams, “Ordered logit models—Basic & intermediate topics,” University of Notre Dame, Indiana, Jan. 2022.

X. Liu, “Fitting stereotype logistic regression models for ordinal response variables in educational research (Stata),” J. Mod. Appl. Stat. Methods, vol. 13, no. 2, pp. 528–545, Nov. 2014, doi: 10.22237/jmasm/1414816200.

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