Optimizing local and global objectives for sustainable mobility in urban areas

Cities are growing and sustainable urban mobility planning (SUMP) is gaining in importance with it. The problems in the domain often involve multiple stakeholders with conflicting or competing objectives. The stakeholders and objectives can be local to certain neighborhoods or apply to the global city-wide scale. We present a methodology to address such problems with the help of modern simulators and multi-objective evolutionary algorithms. The methodology brings all stakeholders to the table and presents to them a near optimal set of alternatives to choose from. As an example, we consider the problem of minimizing vehicular noise in a particular neighborhood while also minimizing city-wide emission for heavy vehicles. We describe the requirements and capabilities of the simulator and the optimization algorithm in detail and present a methodology to model both local (noise reduction) and global (emissions) objectives simultaneously. We apply our methodology on two large city scale case studies and present our findings.