The NISMOD2 Transport Model uses an elasticity-based simulation methodology to facilitate investigation of the future of transport infrastructure systems. The forecasts produced by the model take account of the effects of changes in both exogenous (e.g. population and income) and endogenous (e.g. travel time and travel cost) variables linked to travel. The model can include different future scenarios for vehicle fuel mix, fuel efficiency, transport pricing, infrastructure enhancements and policy decisions. Base year datasets are integrated in the model and are almost entirely open source. Forecasts of future population, GVA and energy price levels are obtained from other elements of the NISMOD system. Scenario-specific inputs regarding endogenous features of transport systems are defined by the user as part of the modelling process.
The road model predicts passenger and freight transport demand and simulates traffic on all major roads in Great Britain. The model is explicitly network-based, in order to obtain more accurate predictions of travel times, travel costs and capacity utilisation. The structure of the road model is shown in Figure 1. It works on a simulation basis, predicting changes in flows from a base year OD matrix calibrated against observed traffic levels. It generates outputs against a range of KPIs, such as journey times, capacity utilisation, fuel consumption, and emissions. The road model is supplemented by models of rail and air transport. The rail model provide station-based forecasts of future demand, linked to the University of Southampton’s integrated station choice and demand model which produces base demand forecasts for new stations. The air model forecasts future demand for domestic and international aviation in Great Britain at a nodal level. All three models run based on common scenarios thereby giving a consistent picture of future travel patterns.
Figure 1: Structure of NISMOD 2 Road Model
Figure 2: Road capacity utilisation
Figure 3: EV electricity consumption in LADs
Spatially detailed forecasts
However, planning for such strategic transport investments is fraught with difficulties, due to their high costs and public profile, long asset life, and uncertainty over future transport demand patterns and technologies. Given that only a finite quantity of funding is available for transport investment, it is important that this funding is spent in the right places and on the right projects (Blainey & Preston, 2019).
The NISMOD transport model can inform transport infrastructure investment decisions by providing spatially detailed forecasts of future transport demand and capacity utilisation.
Oxford-Cambridge Arc analysis
Predict or prophesy? Issues and trade-offs in modelling long-term transport infrastructure demand and capacity
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