Land use transport interaction models have been developed in various forms dating back to the early 1960s. They have moved from spatial interaction models or statistical models through econometric models to micro-simulation, cell-based automata and agent-based models. There has been a move towards more detail in representing space and individual behaviour. This paper presents an alternative approach which uses an intentionally developed strategic high-level model. Nevertheless this model still delivers comparable levels of statistical fi t in terms of validation but is easy to present to decision makers and planners. The paper introduces the concepts behind the MARS model, deals with validation and transferability between cities and provides example applications.

Cities worldwide face problems like congestion or outward migration of businesses. The involved transport and land use interactions require innovative tools. The dynamic Land Use and Transport Interaction model MARS (Metropolitan Activity Relocation Simulator) is part of a structured decision making process. Cities are seen as self organizing systems. MARS uses Causal Loop Diagrams from Systems Dynamics to explain cause and effect relations. MARS has been benchmarked against other published models. A user friendly interface has been developed to support decision makers. Its usefulness was tested through workshops in Asia. This paper describes the basis, capabilities and uses of MARS.

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This paper presents a methodology for the design of optimal transport strategies and the case study results of the methodology for the City of Edinburgh, using the two multi-modal transport/land-use models MARS and TPM. First, a range of policy instruments are optimised in turn and their relative impacts explored. Second, optimisations with and without financial constraints are performed and compared. Although both models produce similar optimal policies, the relative contribution of the instruments differs between models as does the impact on outcome indicators. It is also shown that by careful design it is possible to identify a strategy which costs no more than the do-minimum but which can generate substantial additional benefits. The optimisation methodology is found to be robust, and is able to be used with different transport models, and with and without financial constraints. q