Creating and Validating Hybrid Large-Scale, Multi-Modal Traffic Simulations for Efficient Transport Planning

Highlights: What are the main findings? With the proposed hybrid toolchain, a seamless transfer from the globally assigned mesodemand to a smaller, locally simulated micro-network has been successfully applied to a test case of the larger Munch metropolitan area. The approach allows link-by-link validation between real measurements, as well as the meso- and themicro-simulations; for the test case, both models showed a good fit between simulated and measured traffic flows, but the micro-model showed more realistic results with respect to the meso-model when average link speeds from floating car data were compared. What is the implication of the main finding? With the presented hybrid approach, it will become feasible to efficiently model and simulate large-scale transport scenarios with individual users while enabling a consistent microsimulation on dedicated areas, which are sensitive to the implementation of a wide range of complex transport services and policy measures. It is possible to quantify and directly compare the closeness to reality of the meso- and micro-model, which is useful to demonstrate whether amicro-simulation does offer added value and whether it is worth the additional efforts with respect to the meso-only approach. Mobility digital twins (MDTs), which utilize multi-modal microscopic (micro) traffic simulations and an activity-based demand generation, are envisioned as flexible and reliable planning tools for addressing today’s increasingly complex and diverse transport scenarios. Hybrid models may become a resource-efficient solution for building MDTs by creating large-scale, mesoscopic (meso) traffic simulations, using simplified, queue-based network-link models, in combination with more detailed local micro-traffic simulations focused on areas of interest. The overall objective of this paper is to develop an efficient toolchain capable of automatically generating, calibrating, and validating hybrid scenarios, with the following specific goals: (i) an automated and seamless merge of the meso- and micro-networks and demand; (ii) a validation procedure that incorporates real-world data into the hybrid model, enabling the meso- and micro-sub-models to be validated separately and compared to determine which simulation, micro- or meso-, more accurately reflects reality. The developed toolchain is implemented and applied to a case study of Munich, Germany, with the micro-simulation focusing on the city quarter of Schwabing, using real-word traffic flow and floating car data for validation. When validating the simulated flows with the detected flows, the regression curve shows acceptable values. The speed validation with floating car data reveals significant differences; however, it demonstrates that the micro-simulation achieves considerably better agreement with real speeds compared to the meso-model, as expected.