Dr. Giovanni Circella
MARS-Bari is a fast integrated land use transportation model, which is designed for applications in strategic planning of urban and metropolitan areas. The model was developed as a decision support tool, to help planners in the definition of sets of policies on land use and transportation, and promote equilibrated growth of the urban system (smart growth). MARS-Bari shares the basic assumptions of the Metropolitan Activity Relocation Simulator (MARS) modeling system. It runs in a VenSim environment. The structure of MARS-Bari is highly flexible on purpose, allowing multiple hypothesis tests of future scenarios and policies. MARS-Bari works at a high level of spatial aggregation, and its simulations are developed on a long-term time horizon (30-years in the current version). Two main sub-models run in MARS-Bari: they respectively simulate the development of land use, and the transportation system. The land use model simulates the relocation of both residences and economic activities. It explicitly accounts for two different sectors of the economy, industry and services. Four different classes of users are distinguished in the transportation model, depending on the possible access to one or more of the five transportation modes: car, motorcycle, bus, train, and slow mode (walk and bike). Car and motorcycle ownership and availability functions contribute to the definition of travel patterns and activity participation of users, in dependence of the chosen residential location and of the location of the economic activities. The model was implemented and tested on the metropolitan area of Bari, Italy. MARS-Bari allows the evaluation of different scenarios of future development of the area, predicting the expected outcomes of policies and interventions on land use development, transportation infrastructures, modifications in the local economy, financial regulations and fares (e.g. fuel price, road pricing, congestion charge, transit fares, etc.). The estimation of indicators for environmental quality and pollutant emissions from transportation permits the direct comparison of the outcomes of different scenarios, thus supporting the definition of sets of “second-best” choices for policies in both land use and transportation planning.