Many cities today are struggling with increasing urban car traffic and the associated negative effects such as emissions, air pollution, accidents and noise. To overcome these challenges, cities are investing in public transport services or reducing fares, building cycle paths or implementing measures to reduce car traffic. These include measures such as one-way streets, reduced speed limits or reduced parking spaces and increased parking fees. However, it is important for city administrations to know the effects of their measures in advance, both to anticipate their impact and to discuss them with the public and legitimize their actions. Therefore, this study describes the application of an agent-based travel demand model in combination with a microscopic traffic simulation model to simulate the effects of a baseline scenario and a redesign scenario of urban transport infrastructure on a small spatial scale of an inner-city neighborhood. From the results, we can infer the impact of different measures on mode choice and traffic volumes. The presented methodology show a high potential for planning, evaluation and decision making in the field of sustainable urban mobility.