Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built validated for city of Bologna (Italy) during morning peak hour. Large-scale microsimulations enable evaluation city-wide effects novel complex technologies services, such as intelligent traffic lights or shared autonomous vehicles. can be seen an interdisciplinary project where planners technology developers work together on same scenario; big data from OpenStreetMap, surveys, GPS traces, counts transit details are merged into a unique scenario. The employed activity-based demand model able to simulate evaluate door-to-door trip times while testing different mobility strategies. Indeed, utility-based mode choice calibrated that matches official modal split. implemented analyzed with software SUMOPy/SUMO which open source software, available GitHub. simulated flows compared counters using indicators. determination coefficient has been 0.7 larger roads (width greater than seven meters). present shows it possible build realistic scenarios urban areas. higher precision results could achieved by more coherent merging sources.