DOI: https://doi.org/10.14710/jati.20.3.138-147

A NOVEL DEMAND FORECASTING METHOD USING LOGISTIC REGRESSION AND CONJOINT ANALYSIS FOR PREDICTING THE VOLUME OF NEW TRANSPORTATION ACTIVITIES

*Rully Tri Cahyono scopus  -  Bandung Institute of Technology, Indonesia
Akmalul Adabi  -  Bandung Institute of Technology, Indonesia

Citation Format:
Abstract

In developing new transportation projects, the common approach is to prioritize infrastructure first, with the expectation that demand will follow. This is particularly true for public sector logistics activities, such as seaports, airports, and logistics zones. Unlike the private sector, predicting future demand for public transportation is more challenging due to its complexity. Incorrect demand forecasting can result in improperly sized infrastructure and inefficient human resource allocation. This paper introduces a novel method for forecasting demand in public sector transportation projects. It integrates historical data with the perceptions of transportation stakeholders, improving forecasting accuracy. The method combines conjoint analysis with disaggregated forecasting for each product group. It was applied to a real case namely the Indonesian Ministry of Transportation's plan to develop a cargo transshipment terminal at Denpasar Airport in Bali. A survey of 233 logistics professionals in Jakarta, Bandung, and Denpasar was conducted, and the forecasts were verified through interviews. The results showed that the forecasts for each product category were accurate. This method’s reliability suggests its potential for use in other transportation development projects.

Fulltext View|Download
Keywords: Forecasting; Demand; Conjoint; Transportation

Article Metrics:

Article Info
Section: Research Articles
Language : EN
Recent articles
IMPLEMENTATION OF AGGREGATE PLANNING FOR SMES IN THE MANUFACTURING INDUSTRY EVALUATION OF OFFICE CHAIR DESIGN FROM ERGONOMIC AND ANTHROPOMETRIC PERSPECTIVES EVALUASI USABILITY WEBSITE PRODUK ORGANIK DENGAN METODE SYSTEM USABILITY SCALE OPTIMASI PENGGUNAAN SISTEM HYBRID PANEL SURYA-PLN UNTUK MEMINIMASI BIAYA ENERGI LISTRIK JANGKA PANJANG (STUDI KASUS: 20 TAHUN) More recent articles
Most cited articles
PENGARUH PENERAPAN PERIKLANAN DI INTERNET DAN PEMASARAN MELALUI E-MAIL PRODUK UMKM DI WILAYAH DEPOK KORELASI ANTARA EMPLOYEES INVOLVEMENT, TURNOVER SERTA PENGARUHNYA TERHADAP PRODUKTIVITAS PENILAIAN KUALITAS APLIKASI HALAL MUI DENGAN WEBQUAL 4.0 DAN PENGARUHNYA TERHADAP KEPUTUSAN PENGGUNAAN ERGONOMI PARTISIPASI DALAM MEMPREDIKSI TINGKAT KESEDIAAN UNTUK PERUBAHAN KUALITAS HIDUP KELUARGA TUKANG SAMPAH/ PEMULUNG MODEL PEMILIHAN BAHAN PEWARNA ALAM COKLAT BATIK TULIS SOLO DENGAN MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS (AHP) More cited articles
  1. Abdoli, G. (2020). Comparing the Prediction Accuracy of LSTM and ARIMA Models for Time-Series with Permanent Fluctuation. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3612487
  2. Air Cargo News. (2017). AAPA reports on Asia Pacific airlines 2017 performance. https://www.aircargonews.net/airlines/aapa-reports-on-asia-pacific-airlines-2017-performance/
  3. Angkasa Pura Logistik News. (2025). Kesiapan Pengelolaan Kargo dalam Hal Penjaringan Demand dan Pengembangan Fasilitas. https://bali-airport.com/en/news/index/layani-23-9-juta-penumpang-selama-2024-jumlah-penumpang-bandara-i-gusti-ngurah-rai-naik-12-persen
  4. Aytekin, A., Korucuk, S., & Görçün, Ö. F. (2024). Determining the factors affecting transportation demand management and selecting the best strategy: A case study. Transport Policy, 146, 150–166. https://doi.org/10.1016/J.TRANPOL.2023.11.003
  5. Balakrishnan, A., & Karsten, C. V. (2017). Container shipping service selection and cargo routing with transshipment limits. European Journal of Operational Research, 263(2), 652–663. https://doi.org/10.1016/J.EJOR.2017.05.031
  6. Barros, L., & Hilmola, O. P. (2007). Quantifying and modelling logistics at business and macro levels. International Journal of Logistics Systems and Management, 3(4), 382–394. https://doi.org/10.1504/IJLSM.2007.013208
  7. Bass, F. M. (1969). A New Product Growth for Model Consumer Durables. Https://Doi.Org/10.1287/Mnsc.15.5.215, 15(5), 215–227.
  8. Bunahri, R. R., Supardam, D., Prayitno, H., & Kuntadi, C. (2023). Determination of Air Cargo Performance: Analysis of Revenue Management, Terminal Operations, and Aircraft Loading (Air Cargo Management Literature Review). Dinasti International Journal of Management Science, 4(5), 833–844. https://doi.org/10.31933/DIJMS.V4I5.1822
  9. Chao, C. C., & Yu, P. C. (2013). Quantitative evaluation model of air cargo competitiveness and comparative analysis of major Asia-Pacific airports. Transport Policy, 30, 318–326. https://doi.org/10.1016/J.TRANPOL.2013.10.001
  10. Chung, T. W., Jang, H. M., & Han, J. K. (2013). Financial-based Brand Value of Incheon International Airport. The Asian Journal of Shipping and Logistics, 29(2), 267–286. https://doi.org/10.1016/J.AJSL.2013.08.008
  11. Croxton, K. L., Lambert, D. M., García-Dastugue, S. J., & Rogers, D. S. (2002). The Demand Management Process. The International Journal of Logistics Management, 13(2), 51–66. https://doi.org/10.1108/09574090210806423/FULL/XML
  12. Danielis, R., & Lucia, R. (2002). Shippers’ preferences for freight transport services: a conjoint analysis experiment for Italy
  13. Eisenhardt, K. M. (1989). Building Theories from Case Study Research. The Academy of Management Review, 14(4), 532. https://doi.org/10.2307/258557
  14. Faraport. (2019). CargoCity Frankfurt
  15. Feizabadi, J. (2022a). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119–142. https://doi.org/10.1080/13675567.2020.1803246
  16. Feizabadi, J. (2022b). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119–142. https://doi.org/10.1080/13675567.2020.1803246
  17. Gardiner, J., Ison, S., & Humphreys, I. (2005). Factors influencing cargo airlines’ choice of airport: An international survey. Journal of Air Transport Management, 11(6), 393–399. https://doi.org/10.1016/J.JAIRTRAMAN.2005.05.004
  18. Grzybowska, K., Tubis, A., Kramarz, M., & Kmiecik, M. (2022). Quality of Forecasts as the Factor Determining the Coordination of Logistics Processes by Logistic Operator. Sustainability 2022, Vol. 14, Page 1013, 14(2), 1013. https://doi.org/10.3390/SU14021013
  19. Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010). Multivariate Data Analysis. 785
  20. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). MULTIVARIATE DATA ANALYSIS EIGHTH EDITION
  21. Hess, S., & Polak, J. W. (2006). Airport, airline and access mode choice in the San Francisco Bay area. Papers in Regional Science, 85(4), 543–567. https://doi.org/10.1111/J.1435-5957.2006.00097.X
  22. Hong, S. J., Kim, W., & Niranjan, S. (2023). Challenges to the air cargo business of combination carriers: Analysis of two major Korean Airlines. Journal of Air Transport Management, 108, 102360. https://doi.org/10.1016/J.JAIRTRAMAN.2023.102360
  23. Hudalah, D., Talitha, T., & Lestari, S. F. (2021). Pragmatic state rescaling: The dynamics and diversity of state space in Indonesian megaproject planning and governance. Https://Doi.Org/10.1177/23996544211030935, 40(2), 481–501.
  24. Institute of Transportation. (1999). International Airports Development Trends and Competitiveness Analysis in the Asia Pacific Region. Institute of Transportation Republic of China
  25. Jacquillat, A., & Odoni, A. R. (2018). A roadmap toward airport demand and capacity management. Transportation Research Part A: Policy and Practice, 114, 168–185. https://doi.org/10.1016/J.TRA.2017.09.027
  26. Kofteci, S., & Ergun, M. (2010). Modeling freight transportation preferences: Conjoint analysis for Turkish Region. Scientific Research and Essays, 5. http://www.academicjournals.org/SRE
  27. Larrodé, E., Muerza, V., & Villagrasa, V. (2018). Analysis model to quantify potential factors in the growth of air cargo logistics in airports. Transportation Research Procedia, 33, 339–346. https://doi.org/10.1016/J.TRPRO.2018.10.111
  28. Lee, J., Cho, Y., Lee, J. D., & Lee, C. Y. (2006). Forecasting future demand for large-screen television sets using conjoint analysis with diffusion model. Technological Forecasting and Social Change, 73(4), 362–376. https://doi.org/10.1016/J.TECHFORE.2004.12.002
  29. Loo, B. P. Y. (2008). Passengers’ airport choice within multi-airport regions (MARs): some insights from a stated preference survey at Hong Kong International Airport. Journal of Transport Geography, 16(2), 117–125. https://doi.org/10.1016/J.JTRANGEO.2007.05.003
  30. Ma, H. L., Sun, Y., Mo, D. Y., & Wang, Y. (2023). Impact of passenger unused baggage capacity on air cargo delivery. Annals of Operations Research, 1–17. https://doi.org/10.1007/S10479-023-05248-Y/TABLES/3
  31. María, J., Rodríguez, A., Martinovi´cmartinovi´c, M., Dokic, K., & Pudi´c, D. P. (2025). Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach. Applied Sciences 2025, Vol. 15, Page 3636, 15(7), 3636. https://doi.org/10.3390/APP15073636
  32. Pels, E., Njegovan, N., & Behrens, C. (2009). Low-cost airlines and airport competition. Transportation Research Part E: Logistics and Transportation Review, 45(2), 335–344. https://doi.org/10.1016/J.TRE.2008.09.005
  33. Prasetyo, A., Sulistio, H., & Wicaksono, A. (2015). Kajian Kinerja Pelayanan Terminal Kargo Domestik Di Bandar Udara Juanda Surabaya. Rekayasa Sipil, 9(3), 179–190. https://rekayasasipil.ub.ac.id/index.php/rs/article/view/312
  34. Roszkowska, E. (2013). Rank Ordering Criteria Weighting Methods – a Comparative Overview. Optimum. Studia Ekonomiczne, 5(65), 14–33. https://doi.org/10.15290/OSE.2013.05.65.02
  35. Smirnova, E., Hajiyev, N., Glazkova, I., & Hajiyeva, A. (2024). Production companies: Evaluation of accessibility and efficiency of transportation and manufacturing processes. The Asian Journal of Shipping and Logistics, 40(1), 52–60. https://doi.org/10.1016/J.AJSL.2024.01.002
  36. Stylos, N., Zwiegelaar, J., & Buhalis, D. (2021). Big data empowered agility for dynamic, volatile, and time-sensitive service industries: the case of tourism sector. International Journal of Contemporary Hospitality Management, 33(3), 1015–1036. https://doi.org/10.1108/IJCHM-07-2020-0644/FULL/XML
  37. Sultanbek, M., Adilova, N., Sładkowski, A., & Karibayev, A. (2024). Forecasting the demand for railway freight transportation in Kazakhstan: A case study. Transportation Research Interdisciplinary Perspectives, 23, 101028. https://doi.org/10.1016/J.TRIP.2024.101028
  38. Tam, M. C. Y., & Tummala, V. M. R. (2001). An application of the AHP in vendor selection of a telecommunications system. Omega, 29(2), 171–182. https://doi.org/10.1016/S0305-0483(00)00039-6
  39. Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517. https://doi.org/10.1016/J.JBUSRES.2020.09.009
  40. Wang, Q., Liu, X., Huo, B., & Zhao, X. (2023). Economic or relational first? Establishing the competitiveness of third-party logistics information sharing by devoting specific assets and mutual trust. International Journal of Production Economics, 261, 108869. https://doi.org/10.1016/J.IJPE.2023.108869
  41. Wilson, J. R., & Lorenz, K. A. (2015). Introduction to Binary Logistic Regression. In J. R. Wilson & K. A. Lorenz (Eds.), Modeling Binary Correlated Responses using SAS, SPSS and R (pp. 3–16). Springer International Publishing. https://doi.org/10.1007/978-3-319-23805-0_1
  42. Wong, C. W., Cheung, T. K. Y., & Zhang, A. (2023). A connectivity-based methodology for new air route identification. Transportation Research Part A: Policy and Practice, 173, 103715. https://doi.org/10.1016/J.TRA.2023.103715
  43. World Bank. (2023). Logistics Performance Index. https://lpi.worldbank.org/sites/default/files/2023-04/LPI_2023_report.pdf
  44. Y. Park, & Y. Kim. (2003). A Study on the Strategies to attract Transshipment Container Cargoes in Korea. Journal of Logistics Association, 13
  45. Yeo, G. T., Roe, M., & Dinwoodie, J. (2008). Evaluating the competitiveness of container ports in Korea and China. Transportation Research Part A: Policy and Practice, 42(6), 910–921. https://doi.org/10.1016/J.TRA.2008.01.014
  46. Yin, R. K. (2013). Case Study Research: Design and Methods (Applied Social Research Methods). 312
  47. Yu, C., & Zou, L. (2022). Air Trade, Air Cargo Demand, and Network Analysis: Case of the United States. 9, 207–239. https://doi.org/10.1108/S2212-160920220000009009
  48. Zhang, A. (2003). Analysis of an international air-cargo hub: the case of Hong Kong. Journal of Air Transport Management, 9(2), 123–138. https://doi.org/10.1016/S0969-6997(02)00066-2

Last update:

No citation recorded.

Last update: 2025-09-03 08:44:25

No citation recorded.