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



Illustrative examples of the kind of results which the models can produce are shown in the following visualisations.

Figure 2: Road capacity utilisation

Figure 3: EV electricity consumption in LADs



Spatially detailed forecasts
Effective planning and investment for transport infrastructure systems is seen as key for economic development in both advanced and developing economies.

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
The model has already been used as part of the broader ITRC analysis of infrastructure futures for the Oxford-Cambridge Arc. It has also played a key role in the development of scenarios leading to a zero-carbon transport future for England’s Economic Heartland.


Predict or prophesy? Issues and trade-offs in modelling long-term transport infrastructure demand and capacity

Effective planning and investment for transport infrastructure systems is seen as key for economic development in both advanced and developing economies. However, planning for such strategic ... read more

Designing a road traffic model for the cross-sectoral analysis of future national infrastructure

5th International Symposium for Next Generation Infrastructure, London, 11-13 September 2017. read more

Changing commutes and the changing future of urban transport

Urban Studies Seminar on Diversity of Work ‘Places’ and Spaces in Cities, Southampton, 11-12 September 2017. read more



Pathways to decarbonise transport in England’s Economic Heartland

Pathways to decarbonise transport in England’s Economic Heartland

The study assessed outputs from the NISMOD transport model to investigate the effects of population change on travel demand, and the subsequent impact of different sets of options, or ‘Pathways’, read more

ITRC transport modelling for the OxCam Arc

ITRC transport modelling for the OxCam Arc

Dr Simon Blainey, Associate Professor in Transportation within Engineering and Physical Sciences, University of Southampton, outlines his recent research into road transport possibilities for the read more