TIP City Region /Metro Simulator

During the first phase of the development of the TIP Simulator, two prototype simulators were developed for the Gauteng City Region at the level of transport or sub-transport analysis zone: (a) a city-region focused living space and urban growth simulator and (b) a city-region transport demand and supply simulator.  Some results of these are shown in the Demonstrator Simulator.

The objective of the next phase of development is to enhance and refine the prototype city-region growth simulator developed for the Gauteng city-region and to extend the spatial focus of the simulation to include the Cape Town, Ethekwini, and Nelson Mandela Bay city-regions.

The key objectives of the development of the TIP City Region / Metro Simulator are to:

1. Upgrade the simulation platform to UrbanSim, one of the leading Open Source Software simulation platforms in the world.
2. Formally collaborate with the developers of UrbanSim to ensure best practice implementation and joint adaptation of UrbanSim for best results in a developing country like South Africa.
3. Expand the spatial focus of the prototype growth simulator from the case study region to include the Cape Town, Ethekwini, Gauteng, and Nelson Mandela Bay city-regions. 
4. Extend the temporal focus of the city-region growth simulator to approximately 2020/2030.
5. Demonstrate the value of the model by simulating a number of scenarios, (which could be different for each city-region) and comparing the performance of different scenarios with respect to a range of indicators used to characterise the supply and demand of land, housing and infrastructure as well as travel times, modes, cost, etc.

The urban simulation will allow planners to simulate and evaluate (compare) the outcome (expressed in the form of indicators) of various policy scenarios.

The term policy scenario is used here in a very specific way: it is a unique combination of policy alternatives and assumptions, such as estimates of the growth (or decline) of population and employment in the study area.  Policy alternatives may include anything from growth boundaries, zoning and building density restrictions to strategic decisions about transportation and other infrastructure systems.

The term indicator is also used in a specific way: it refers to any data field or variable calculated from one or more data fields in the UrbanSim database.

A trivial example may help to explain this.  If a living laboratory (as proxy for the city or city-region) decides that it is important to focus on the provision of land for informal settlements resulting from an influx of people from other provinces or countries, one could specify two scenarios: the usual trend scenario and a ‘high influx; scenario.  When both scenarios have been simulated, the representatives from the relevant city/city-region will then evaluate (make sense of what the simulation results mean for decision making) by comparing one or more indicators.  In this trivial example, the indicator might be as simple as the tracking the vacancy rate (number of unoccupied units divided by the maximum number of housing units for the appropriate land use and development type) over the simulation period.