Abstract
This paper uses the Extreme Value Theory (EVT) to model the rare events that appear as delivery delays in road transport. Transport delivery delays occur stochastically. Therefore, modeling such events should be done using appropriate tools due to the economic consequences of these extreme events. Additionally, we provide the estimates of the extremal index and the return level with the confidence interval to describe the clustering behavior of rare events in deliveries. The Generalized Extreme Value Distribution (GEV) parameters are estimated using the maximum likelihood method and the penalized maximum likelihood method for better small-sample properties. The findings demonstrate the advantages of EVT-based prediction and its readiness for application.
Highlights
The Extreme Value Theory (EVT) evaluates both the magnitude and frequency of rare events and supports long-term forecasting
The maximum likelihood (ML) method is applied to estimate the parameters of the Generalized Extreme Value (GEV) distribution
The purpose of this paper was to model and forecast rare events that appear as delivery delays in road transport using the Extreme Value Theory (EVT)
Summary
The Extreme Value Theory (EVT) evaluates both the magnitude and frequency of rare events and supports long-term forecasting. Delivery delays in transportation occur stochastically and rarely. They always increase costs, decrease consumers’ satisfaction, and lower the confidence of subcontractors in the supply chain. This paper refers to information-based vehicle planning and route analysis in road freight transportation. Telematics systems track the vehicles and send data to integrated transport management systems (TMS). They help to anticipate extreme time delays when both the transport operator and subcontractors have delivered a fleet
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