Application of aggregate container terminal data for the development of time-of-day models predicting truck arrivals

Abstract

Workload forecasting related to truck arrivals is of essential importance not only for optimal resource allocation but also for encountering delays and bottlenecks in the landside operations of terminals. In order to increase their efficiency, some ports have tried to implement various methods such as Truck Appointment Systems (TAS) but, in general, information on the arrival times of trucks to pick up containers remains unreliable and scarce. This paper sought to develop pick-up time-of-day models for import containers using data easily retrieved from Terminal Operating Systems (TOS). Model results indicate that the receiver of goods and container characteristics are among the main factors affecting pick-up time-of-day. Differentiation is observed between the different days of the week. The developed Time-Of- Day (TOD) models can be used to calculate the probability of drayage truck arrival times. The application of the proposed methodology proves helpful when reliable information of truck arrivals is unavailable, and can also be used alongside TAS implementation to assist terminal operators.