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.

The present study contributes to narrowing down the research gap in modeling individual door-to-door trips in a superblock scenario and in evaluating the respective impacts in terms of travel times, modal shifts, traffic performance, and environmental benefits. The methods used are a multiple-criteria approach to identify the superblocks and a large-scale, multi-model, activity-based microscopic simulation. These methods were applied to the city of Bologna, Italy, where 49 feasible superblocks were identified. A previous large-scale microscopic traffic model of Bologna is leveraged to build a baseline scenario. A superblock scenario is then created to model five proposed traffic intervention measures. Several mobility benefit indicators at both citywide and superblock levels are compared. The simulation results indicate a significant increase in walking time for car drivers, while the average waiting time of bus users decreases due to the increased frequency of bus services. This leads to a noticeable car-to-bus shift. In addition, absolute traffic volumes and traffic-related emissions decreased significantly. Surprisingly, traffic volumes on the roads around the superblocks did not increase as expected. In general, this research provides scientists and urban and transport planners with insights into how changes in door-to-door travel times of multi-modal trips can impact individual travel behavior and traffic performance at a citywide level. However, the study still has limitations in modeling the long-term effects regarding changing activity locations within the superblocks.

Large-scale traffic simulations at a microscopic level can mimic the physical reality in great detail so that innovative transport services can be evaluated. However, the simulation times of such scenarios is currently too long to be practical. (1) Background: With the availability of Graphical Processing Units (GPUs), is it possible to exploit parallel computing to reduce the simulation times of large microscopic simulations, such that they can run on normal PCs at reasonable runtimes?; (2) Methods: ParSim, a microsimulator with a monolithic microsimulation kernel, has been developed for CUDA-compatible GPUs, with the aim to efficiently parallelize the simulation processes; particular care has been taken regarding the memory usage and thread synchronization, and visualization software has been optionally added; (3) Results: The parallelized simulations have been performed by a GPU with an average performance, a 24 h microsimulation scenario for Bologna with 1 million trips was completed in 40 s. The average speeds and waiting times are similar to the results from an established microsimulator (SUMO), but the execution time is up to 5000 times faster with respect to SUMO; the 28 million trips of the 24 h San Francisco Bay Area scenario was completed in 26 min. With cutting-edge GPUs, the simulation speed can possibly be further reduced by a factor of seven; (4) Conclusions: The parallelized simulator presented in this paper can perform large-scale microsimulations in a reasonable time on readily available and inexpensive computer hardware. This means microsimulations could now be used in new application fields such as activity-based demand generation, reinforced AI learning, traffic forecasting, or crisis response management.

Large scale traffic simulations at a microscopic level can mimic the physical reality to a great detail and innovative transport services can be evaluated. However, the simulation time of such scenarios is currently too long to be practical. (1) Background: with the availability of Graphical Processing Units (GPUs), is it possible to exploit parallel computing to reduce the simulation time of large microscopic simulations such that they can run on normal PCs at reasonable run-times?; (2) Methods: ParSim, a microsimulator with a monolithic microsimulation kernel has been developed for CUDA compatible GPUs, with the aim to efficiently parallelize the simulation processes, particular care has been taken of the memory usage and thread synchronization, and visualization software has been added optionally; (3) Results: the parallelized simulations have been performed by a GPU with an average performance, the 24h microsimulation scenario of Bologna with 1 million trips has been completed in 40s. Average speeds and waiting times are similar to the results from an established microsimulator (SUMO), but up to 5,000 times faster; the 28 million trips of the 24h San Francisco Bay Area scenario has been completed in 26min. With cutting edge GPUs the simulation speed can possibly be reduced by a factor of 7. (4) Conclusions: the parallelized simulator presented in this paper can perform large scale microsimulation in a reasonable time on available and inexpensive computer hardware. This means microsimulations could now be used in new application fields such as activity-based demand generation, reinforced AI learning, traffic forecasting or crises response management.