National Infrastructure Systems Model setup, configuration and tests
Model code: nismod/transport
Key reference: Lovric, M., Blainey, S., & Preston, J. (2018). A conceptual design for a national transport model with cross-sectoral interdependencies. Transportation Research Procedia, 27, 720-727
Details of model inputs, parameters and outputs:
Notes on data sources:
NISMOD v2 Transport Model [1] is a national-scale (Great Britain) transport model developed to support policy making regarding future infrastructure. It forecasts the impact of various endogenous and exogenous factors on transport demand and capacity utilisation, following an elasticity-based simulation methodology similar to the original ITRC model (NISMOD v1). The new model, however, is explicitly network-based, in that that the demand is assigned to the transport network to obtain more accurate predictions of travel times, travel costs and capacity utilisation.
The NISMOD v2 Transport Model predicts vehicle demand (inter-zonal flows) for passenger and freight vehicles, and stochastically simulates road traffic on all major UK roads including A-roads and motorways. The number of lanes on each road segment has been estimated by map-matching AADF count point locations to the OpenRoads major road network. This has allowed a distinction between single and dual carriageway A-roads, which are then assumed to have 1 and 2 lanes per direction, respectively.
It is currently the only national-scale road traffic model capable of routing-based network assignment and provisioning a national-scale origin-destination matrix (on TEMPRo & LAD spatial zoning levels), while achieving a respectable match with AADF traffic counts, total vehicle kilometres, expected number of car trips, and the observed trip length distribution from the National Travel Survey. The freight model has been modelled after the DfT’s 2006 Base-Year Freight Matrices model, which includes traffic flows for freight vehicles (vans, rigid HGVs, and articulated HGVs) between local authority districts (LADs), sea ports, selected airports, and major distribution centres. The accuracy of the freight model is mostly limited by the spatial zoning system (LAD).
Demand prediction for the transport model is given by an elasticity-based model that can predict future vehicle flows from exogenous (scenario-based) changes in population and GVA, and endogenously calculated changes in inter-zonal travel time and travel cost (but also dependent on exogenous interventions such as new road development and congestion charging policies).
Congested travel times on individual road links have been modelled separately for each hour of the day, using the speed-flow curves estimated on English roads (DfT’s 2005 FORGE model), the overcapacity formula from WebTAG, and the passenger car unit (PCU) concept to capture different vehicle sizes.
The network assignment exists in two versions and has been implemented using state- of-the-art routing algorithms. The routing version uses an A* heuristic search algorithm to find the fastest path between two locations using congested link travel times, while the route-choice version uses an advanced discrete-choice model (path-size logit) to choose the optimal path based on distance, travel time, travel cost (fuel and road tolls), and the number of intersections.
The route-choice version of the network assignment uses a pre-generated route set, which consists of more than 90 million different route options, enabling the national- scale assignment to run within minutes, despite each individual vehicle trip being simulated separately (including time of day choice, engine type choice, route choice). The model can assess different scenarios of fuel efficiency and engine type market share (i.e. internal combustion engines on petrol, diesel, LPG, hydrogen or CNG; hybrid EVs on petrol or diesel; plug-in hybrid EVs on petrol or diesel; fuel cell EVs on hydrogen, and battery EV). This scenario analysis can be used to test policies such as the fossil fuel phase-out.
Electricity and fuel consumption are calculated using the four-parameter formula from WebTAG. Behavioural assumptions are made for plug-in hybrid EVs (electricity on urban, fuel on rural road links).
Interventions such as new road development, road expansion with new lanes, and congestion charging zones can be dynamically implemented in each simulated year. The model can output various metrics at the road link level (e.g. road capacity utilisation, peak hour travel times), zonal level (e.g. vehicle kilometres, EV electricity consumption), inter-zonal level (e.g. predicted vehicle flows, average travel times, average travel costs) and national level (e.g. total CO₂ emissions, total energy consumptions). The outputs are in csv and shapefile format, allowing them to be visualised with a software of choice.
The NISMOD v2 Transport Model also includes a national-scale rail model for predicting future station usage, using base year data for 3054 stations covering National Rail, London Underground, Docklands Light Railway, London Trams (previously Croydon Tramlink), Manchester Metrolink, and Tyne & Wear (Newcastle) Metro.
The demand model is elasticity-based, and can predict station usage (entry + exit) from exogenous inputs including: population, GVA, rail fare index, generalised journey time (GJT) index and car trip costs (which can be provided as an input or calculated from the outputs of the NISMOD road model). Demand elasticities of rail fares and GJT vary between different areas of the country (London Travelcard, South-East, PTE, other).
The model capabilities include an assessment of building new rail stations in future years.
DAFNI dataset: v2.3.0
Contains data required by the NISMOD Transport model.
Includes the strategic road network (motorways and A roads), TEMPRO zone definitions, stations, modelled passenger and freight origin-destination matrices, modelled route options, future interventions (congestion charging, lane expansions, new road links, new stations), parameters and scenarios for fuel efficiency, costs, trip rates, time of day distributions for trips, future composition of the road fleet (vehicle and engine type), and default model elasticity parameters (for time, cost, population and GVA).
Contains data covered by the Open Government Licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ Contains National Statistics data © Crown copyright and database right 2012. Contains Ordnance Survey data © Crown copyright and database right 2012.
Road network and AADF traffic counts data are derived from Department for Transport GB Road Traffic Counts: - https://data.gov.uk/dataset/208c0e7b-353f-4e2d-8b7a-1a7118467acc/gb-road-traffic-counts
Railway stations are largely derived from the NaPTAN extract, supplemented with:
Particularly for model calibration, origin-destination matrix generation and route-set generation, we acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton.
All data compiled by Milan Lovric and Simon Blainey, University of Southampton.