Analysis of the Transport Problem with the Logistic Regression Model
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Abstract
The objective of this research is to study and analyze transportation problems, which are critical issues occurring within the business sector providing logistics system services. The findings can be applied by related organizations to forecast business activities in the transportation service industry. The analysis employs mathematical and statistical techniques, specifically applying the logistic regression model. The study uses secondary data obtained from the Thai Logistics Association. The identified independent variables related to the economic viability of transportation service providers include transportation costs, the distance trips, and the average service rate. The dependent variable is the economic viability of accepting transportation jobs among logistics service providers. The study found that the developed logistic regression model could predict the dependent variable with an accuracy of 46.90%, which is considered acceptable and applicable in business contexts. Additionally, the researchers employed the Hosmer and Lemeshow test to assess the model’s goodness-of-fit, and the result showed a p-value of 0.581. This indicates that the model is appropriate and statistically significant for forecasting transportation problems at the 0.05 significance level.
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References
ณัฐวุฒิ งามสุทธิ์. (2553). การศึกษาตัวแบบการขนส่งระบบโลจิสติกส์ขาเข้าของโรงงานอุตสาหกรรมแปรรูปไม้ยางพารา: กรณีศึกษา 5 จังหวัดภาคใต้ (วิทยานิพนธ์ปริญญามหาบัณฑิต).มหาวิทยาลัยสงขลานครินทร์.
ภัคสุภางค์ มาปรีดา. (2560). ตัวแบบการถดถอยลอจิสติกในการพยากรณ์ความน่าจะเป็นของการชำระหนี้ได้ของครัวเรือน: กรณีศึกษา จังหวัดปทุมธานี (สารนิพนธ์ปริญญามหาบัณฑิต).มหาวิทยาลัยธรรมศาสตร์.
สิทธิศักดิ์ จุลเชาว์ และ โกวิท รพีพิศาล. (2561). การวิเคราะห์ถดถอยโลจิสติกส์พหุกลุ่มเพื่อพัฒนาตัวแบบอิทธิพลการยอมรับและใช้เทคโนโลยีสำหรับรถโดยสารประจำทางในจังหวัดขอนแก่น: ศึกษาเฉพาะกรณี KK Transit. วารสาร Veridian E-Journal มหาวิทยาลัยศิลปากร, 11(2), 2900–2916.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Holguín-Veras, J. (2000). Revealed preference analysis of commercial vehicle choice process. Journal of Transportation Engineering, 128(4), 336–346.
Jayasingh, S., & Eze, U. C. (2015). An empirical analysis of consumer behavioral intention towards mobile apps. Journal of Internet Banking and Commerce, 20(3), 1–19.
Katz, E., Levin, M., & Hamilton, H. (1963). Traditions of research on the diffusion of innovation. American Sociological Review, 28(2), 237–252.
Kelton, D. W., Sadowski, R. P., & Sturrock, D. T. (2003). Simulation with Arena (3rd ed.). McGraw-Hill.
King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political Analysis, 9(2), 137–163. https://doi.org/10.1093/oxfordjournals.pan.a004868
Maria, A. (1997). Introduction to modeling and simulation. In S. Andradottir, K. J. Healy, D. H. Withers, & B. L. Nelson (Eds.). Proceedings of the 1997 Winter Simulation Conference (pp. 7–13). IEEE.
