Journal
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 38, Issue -, Pages 73-84Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2013.10.012
Keywords
Spatio-temporal data mining; Intermodal transport; Support vector machines; Mixed integer programming
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Complexity in transport networks evokes the need for instant response to the changing dynamics and uncertainties in the upstream operations, where multiple modes of transport are often available, but rarely used in conjunction. This paper proposes a model for strategic transport planning involving a network wide intermodal transport system. The system determines the spatio-temporal states of road based freight networks (unimodal) and future traffic flow in definite time intervals. This information is processed to devise efficient scheduling plans by coordinating and connecting existing rail transport schedules to road based freight systems (intermodal). The traffic flow estimation is performed by kernel based support vector mechanisms while mixed integer programming (MIP) is used to optimize schedules for intermodal transport network by considering various costs and additional capacity constraints. The model has been successfully applied to an existing Fast Moving Consumer Goods (FMCG) distribution network in India with encouraging results, (C) 2013 Elsevier Ltd. All rights reserved.
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