A conceptual design for a national transport model with cross-sectoral interdependencies

Abstract

This paper presents issues, trade-offs and challenges encountered while developing a UK national transport model as part of a large interdisciplinary project, ITRC MISTRAL. The Infrastructure Transitions Research Consortium (ITRC) is a consortium of seven leading UK universities focusing on analysis of national infrastructure systems using a system-of-systems approach. In this paper, we describe a multi-modal multi-scale national transport model being developed by ITRC which includes passenger and freight transport via highways, railways, airports, seaports and local transit networks. The model predicts future demand for each mode on individual flows using an elasticity-based simulation approach. These flows are then assigned to transport networks to assess infrastructure capacity utilisation and obtain new estimates of inter-zonal travel times. The model explicitly considers cross-sectoral interdependencies with other infrastructure networks, including the energy sector (where transport is the largest consuming sector), digital communications (which provide bandwidth to passengers and enable smart mobility), waste management (which requires transport services) and water supply (where flooding poses a major risk of transport disruptions). It is also planned to be capable of estimating environmental emissions and assessing the vulnerability and resilience to risk of transport systems. The enhanced transport model discussed here builds on an existing modelling framework which has been used by the UK government to inform their National Infrastructure Assessment. As such, the model has the potential to support policy making with regards to infrastructure investment on a decadal scale, under a range of possible future scenarios including population growth, new technologies and climate change.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 12 citations exist upstream.