Resumen
Integrated land-use transport models are often accused of being too complex, too coarse or too slow. We tightly couple the microscopic land use model SILO (Simple Integrated Land Use Orchestrator) with the agent-based transport simulation model MATSim (Multi-Agent Transport Simulation). The integration of the two models is person-centric. It means, firstly, that travel demand is generated microscopically. Secondly, SILO agents can query individualized travel information to search for housing or jobs (and to choose among available modes). Consequently, travel time matrices (skim matrices) are not needed anymore. Travel time queries can be done for any time of the day (instead of for one or few time periods), any x/y coordinate (instead of a limited number of zones) and take into account properties of the individual. This way, we avoid aggregation issues (e.g., large zones that disguise local differences) and we can account for individual constraints (e.g., nighttime workers who cannot commute by public transport for lack of service). Therefore, the behavior of agents is represented realistically, which allows us to simulate their reaction to novel policies (e.g., emission-class-based vehicle restrictions) and to extract system-wide effects. The model is applied in two study areas: a toy scenario and the metropolitan region of Munich. We simulate various transport and land use policies to test the model capabilities, including public transport extensions, zones restricted for private cars and land use development regulations. The results demonstrate that the increase of the model resolution and model expressiveness facilitates the simulation of such policies and the interpretation of the results.