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Achieving Transport Modal Split Targets at Intermodal Freight Hubs Using a Model Predictive Approach
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Advisor(s)
Abstract(s)
The increase of international freight commerce is creating pressure on the existing
transport network. Cooperation between the different transport parties (e.g., terminal
managers, forwarders and transport providers) is required to increase the network
throughput using the same infrastructure. The intermodal hubs are locations where cargo
is stored and can switch transport modality while approaching the final destination.
Decisions regarding cargo assignment are based on cargo properties. Cargo properties
can be fixed (e.g., destination, volume, weight) or time varying (remaining time until
due time or goods expiration date). The intermodal hub manager, with access to certain
cargo information, can promote cooperation with and among different transport providers
that pick up and deliver cargo at the hub. In this paper, cargo evolution at intermodal hubs
is modeled based on a mass balance, taking into account hub cargo inflows and outflows,
plus an update of the remaining time until cargo due time. Using this model, written in a
state-space representation, we propose a model predictive approach to address the Modal
Split Aware – Cargo Assignment Problem (MSA–CAP). The MSA–CAP concerns the cargo
assignment to the available transport capacity such that the final destination can be
reached on time while taking into consideration the transport modality used. The model
predictive approach can anticipate cargo peaks at the hub and assigns cargo in advance,
following a push of cargo towards the final destination approach. Through the addition
of a modal split constraint it is possible to guide the daily cargo assignment to achieve a
transport modal split target over a defined period of time. Numerical experiments illustrate
the validity of these statements.
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Citation
Nabais, J., Negenborn, R. R., Carmona Benítez, R. B. & Ayala Botto, M. (2015). Achieving Transport Modal Split Targets at Intermodal Freight Hubs Using a Model Predictive Approach. Transport Research Part C – Emerging Technologies, 60, pp. 278-297.