Geographic Economic accessibility (GEA) for freight transport
Patricio Gallardo, PhD Candidate
University of Canterbury
Time & Place
Thu, 29 Mar 2018 14:00:00 NZDT in E530 (Engineering Core Bldg)
All are welcome
Freight transportation has always been essential for trade and prosperity. The geographic economic accessibility to trade has expanded with modern use of fossil fuels. Geographic economic accessibility (GEA) is a measure of the tonne-kilometre (tkm) dispersion for production and supply chains using the existing transport networks, intermodal connections and available energy. The non-renewable nature of transport fuel, carbon emissions from fossil fuels, and price volatility mean that the outlook for trade will involve pressures for change. The question is: what are the most economically efficient infrastructure investments, operational changes and technology developments to achieve high geographic economic accessibility to trade while adapting to the 80% reduction in fossil fuel which is expected over the lifetime of networks and vehicles?
In this paper, we focus on the first part of the GEA trade analysis scheme, where travel demand is estimated upon the execution of a Random Utility Based Multiregional Input Output Model. Given the mathematical representation of the New Zealand Freight Transportation System, we assess the response of mode share and freight flow dispersion to changes in transportation costs.
We explore freight activity and energy consumption under different infrastructure and network configurations, as well as under different assumptions about travel patterns and logistic dispersion. Such a scheme can be used for the identification of transportation infrastructure that has the potential to enhance a smooth transition of the freight transportation system towards a more resilient configuration that allows for the consolidation of freight flows and for the deployment of more energy efficient modes of transportation. Furthermore we propose the adoption of essentiality metrics over the transportation demand in order to identify what sectors are at higher risk due to lack of capacity to adapt to lower energy consumption.