A Comparative Model Study on the Intermittent Demand Forecast of Air Cargo - Focusing on Croston and Holts models -

2021 ◽  
Vol 37 (1) ◽  
pp. 71-85
Author(s):  
Byung-Cheol Yoo ◽  
Young-Tae Park
2000 ◽  
Vol 105 (C2) ◽  
pp. 3233-3241 ◽  
Author(s):  
N. C. Wells ◽  
V. O. Ivchenko ◽  
S. E. Best

1999 ◽  
Vol 6 (1) ◽  
pp. 53-68 ◽  
Author(s):  
N. Gurung ◽  
Y. Iwao

2014 ◽  
Vol 25 (3) ◽  
pp. 656-672 ◽  
Author(s):  
Subhro Mitra ◽  
Steven M. Leon

Purpose – The purpose of this paper is to develop a better understanding of the factors that influence a shipper's decision to choose air cargo as a mode of shipment. Design/methodology/approach – A disaggregate multinomial discrete choice model is developed using freight shipment survey data to identify critical factors influencing air cargo mode choice. Disaggregate revealed preference data is obtained from surveying 347 manufacturers, freight forwarders, and other third-party service providers. Findings – The empirical model developed in this research shows that the rate of shipment, time of transit, cost-per-pound shipped, quantity shipped, perishability and delay rate of the mode are significant factors that influence mode choice. Research limitations/implications – The discrete choice model developed can be improved by taking into account logistics costs not considered in this research. Perhaps more in-depth surveys of the shippers and freight forwarders are needed. Additionally, improving the mode choice model by including stated preference data and subsequently incorporating service quality latent variables would be beneficial. Practical implications – Identifying the sensitivity of the shippers to various factors influencing mode selection enables transportation planners make better demand forecast for each mode of transportation. Originality/value – This paper extends previous mode choice studies by analyzing mode selection between air cargo and other modes. Better forecasting is achieved by replacing the logit model with probit, heteroscedastic extreme value and mixed logit models.


2018 ◽  
Vol 0 (2.37) ◽  
pp. 44-50
Author(s):  
S.S. Podpriatov ◽  
S.E. Podpriatov ◽  
G.S. Marinsky ◽  
O.V. Chernets ◽  
V.A. Tkachenko ◽  
...  

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