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Increasing Model Precision Can Reduce Accuracy

Author

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  • Carlos F. Daganzo

    (University of California, Berkeley, California)

Abstract

In the field of logistics, a variable that is to be predicted (e.g., cost) often varies in a nonsmooth, irregular, but known manner, with various factors (e.g., distances, quantity, and density of material to be carried, etc.). This paper identifies conditions, where given approximate input factors, a prediction of the variable is less error prone if one uses a smooth approximation to the exact function of the factors. This phenomenon, which is quite prevalent, may enhance the appeal of continuous approximation models in some instances.

Suggested Citation

  • Carlos F. Daganzo, 1987. "Increasing Model Precision Can Reduce Accuracy," Transportation Science, INFORMS, vol. 21(2), pages 100-105, May.
    • Handle: RePEc:inm:ortrsc:v:21:y:1987:i:2:p:100-105
    DOI: 10.1287/trsc.21.2.100
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    Cited by:

    1. Jalil, M.N. & Zuidwijk, R.A. & Fleischmann, M. & van Nunen, J.A.E.E., 2009. "Spare Parts Logistics and Installed Base Information," ERIM Report Series Research in Management ERS-2009-002-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. M N Jalil & R A Zuidwijk & M Fleischmann & Jo A E E van Nunen, 2011. "Spare parts logistics and installed base information," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 442-457, March.
    3. Calabrò, Giovanni & Araldo, Andrea & Oh, Simon & Seshadri, Ravi & Inturri, Giuseppe & Ben-Akiva, Moshe, 2023. "Adaptive transit design: Optimizing fixed and demand responsive multi-modal transportation via continuous approximation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    4. Ansari, Sina & Başdere, Mehmet & Li, Xiaopeng & Ouyang, Yanfeng & Smilowitz, Karen, 2018. "Advancements in continuous approximation models for logistics and transportation systems: 1996–2016," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 229-252.
    5. Giovanni Calabro' & Andrea Araldo & Simon Oh & Ravi Seshadri & Giuseppe Inturri & Moshe Ben-Akiva, 2021. "Adaptive Transit Design: Optimizing Fixed and Demand Responsive Multi-Modal Transportation via Continuous Approximation," Papers 2112.14748, arXiv.org, revised Jan 2023.

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