scholarly journals THE DEVELOPMENT OF INTERMODAL FREIGHT TRANSPORT IN POLAND FROM 2007 TO 2016: BARRIERS AND OPPORTUNITIES TO ITS FURTHER DEVELOPMENT

2021 ◽  
Vol 11 (1) ◽  
Keyword(s):  
Freight Transport ◽  
Intermodal Freight ◽  
Barriers And Opportunities ◽  
Further Development

scholarly journals The imperative for commodity-based intermodal freight transport demand modelling: a South African case study

2014 ◽  
Vol 12 (1) ◽  
pp. 193-202
Author(s):  
Jan Hendrik Havenga ◽  
Zane P. Simpson ◽  
Anneke de Bod
Keyword(s):  
South Africa ◽  
South African ◽  
Freight Transport ◽  
Container Terminals ◽  
Market Segments ◽  
Demand Modelling ◽  
Intermodal Freight ◽  
Transport Demand Modelling

Container forecasting typically focuses on its intermodal nature, container sizes and port container terminals. This leads to a commodity-blind approach to container forecasting, where the twenty-foot-equivalent is the forecasting output. The standardized unit is also increasing into many non-standard forms, indicated by the three main container market segments. This research deconstructs these segments and provides methodological and actual commodity-based container forecasting results for South Africa where intermodal solutions are still in its infancy and investments need to be made based on accurate forecasting

The Merit of Agents in Freight Transport

2009 ◽  
pp. 323-341
Author(s):  
Tamás Máhr ◽  
F. Jordan Srour ◽  
Mathijs de Weerdt ◽  
Rob Zuidwijk
Keyword(s):  
Freight Transport ◽  
Transport Network ◽  
Optimization Approach ◽  
Comparative Performance ◽  
Agent Based ◽  
Solution Approach ◽  
On Line ◽  
Intermodal Freight ◽  
Transport Method

While intermodal freight transport has the potential to introduce efficiency to the transport network,this transport method also suffers from uncertainty at the interface of modes. For example, trucks moving containers to and from a port terminal are often uncertain as to when exactly their container will be released from the ship, from the stack, or from customs. This leads to much difficulty and inefficiency in planning a profitable routing for multiple containers in one day. In this chapter, the authors examine agent-based solutions as a mechanism to handle job arrival uncertainty in the context of a drayage case at the Port of Rotterdam. They compare their agent-based solution approach to a wellknown on-line optimization approach and study the comparative performance of both systems across four scenarios of varying job arrival uncertainty. The chapter concludes that when less than 50% of all jobs are known at the start of the day then an agent-based approach performs competitively with an on-line optimization approach.

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