Addressing Challenges in Creating Traffic Profiles for Transshipment Hubs in Seaports

EasyChair Preprint 16000

Abstract

Globalization has significantly increased containerized traffic, driven by the rising demand for swift cargo movements at low cost. When creating cost- and time-efficient maritime transport networks, transshipment hubs are of high importance. There, containers are moved from one vessel to another. This enables carriers to design hub-and-spoke networks(where feeder vessels serve the spokes and deep sea vessels interconnect the hubs) as well as connecting deep sea services by interlining. Hubs are often located along major shipping routes and are concentrated near canals and straits. The successful operation of transshipment hubs relies on various socio-economic factors, trade policies, and robust infrastructure. When making strategic decisions, simulation is often used to estimate the impact of each viable option on terminal performance. Such simulation studies heavily depend on suitable synthetic traffic profiles that reflect the workload and yard occupancy of transshipment hubs over a longer time horizon. Past work has shown that for transshipment hubs, the expected average yard occupancy is approximated over the course of several weeks, which increases the runtime of simulation studies. The approach presented in this paper addresses this issue by modifying the code of library ConFlowGen and applying it on three use cases. The results show that the traffic profiles generated with the modified code are more suitable for simulating operations of transshipment hubs. Several traffic profile characteristics are discussed, that are difficult to satisfy at the same time.

Keyphrases: ConFlowGen, Port Planning, Synthetic Data Generation, container terminal, maritime transport

@booklet{EasyChair:16000,
  author    = {Shubhangi Gupta and Marvin Kastner and João Vieira and Carlos Jahn},
  title     = {Addressing Challenges in Creating Traffic Profiles for Transshipment Hubs in Seaports},
  howpublished = {EasyChair Preprint 16000},
  year      = {EasyChair, 2025}}