hybridPy: The Simulation Suite for Mesoscopic and Microscopic Traffic Simulations - Joerg Schweizer; Fabian Schuhmann; Cristian Poliziani (SUMO Conference Proceedings, 2024)
Mesoscopic, agent-based simulations efficiently model and assess entire regions’ daily activities and travel patterns, exemplified by smaller countries like Switzerland. The queue-based simulation represents a compromise between computational speed on the one hand and the necessity of detailed modeling infrastructure on the other hand. Thus, mesoscopic simulations enable an efficient and reasonably detailed analysis of the complex interplay between supply and demand in mobility research. Conversely, microsimulations excel at reproducing individual speed profiles and behavior by modeling the interactions between traffic participants, including pedestrians, bicycles, and scooters. Although allowing for more detailed system analysis, the downside is the high computational burden, which often prevents large-scale microscopic simulations from running in optimization or calibration loops. hybridPY, an extension of SUMOPy, aims to close the gap and benefit from both environments. The simulation suite allows the running of mesoscopic as well as microscopic traffic simulations based on the core idea: running a microscopic simulation in a smaller dedicated area, using the routes or mobility plans generated from a larger mesoscopic model. The main features of this software are: (i) import, editing and visualization of MATSim and BEAM CORE networks; (ii) conversion of MATSim plans to SUMO routes or plans within the SUMO area; (iii) configuring and running of MATSim simulations. The capability of hybridPY is demonstrated by two applications: the simulation of Schwabing, Germany, based on the MITO MATSim model, and the San Francisco municipality, USA, based on the mesoscopic BEAM CORE model of the entire San Francisco Bay area. Both scenarios demonstrate that the hybrid approach results in significant computational gains with respect to a pure microscopic approach.