DiCoSim: A Distributed Coordination Framework for Boundary-Consistent Large-Scale Microscopic Traffic Simulation

City-scale microscopic traffic simulation is increasingly used for policy evaluation, operational planning, and disruption analysis, where repeated scenario runs must retain vehicle-level trajectories rather than only aggregate traffic indicators. This requirement creates an efficiency–consistency trade-off: single-node simulators preserve centralized state ownership but become inefficient for million-vehicle tasks, whereas distributed execution reduces runtime but may disrupt vehicle updates at partition boundaries. Cross-partition movement can cause trajectory breaks, duplicate or missing updates, and inconsistent local interaction states if boundary events, vehicle context, and update ownership are not coordinated. To address this problem, this study proposes DiCoSim, a distributed coordination framework for boundary-consistent large-scale microscopic traffic simulation. DiCoSim integrates incremental spectral-clustering partitioning, spatio-temporal event aggregation, and acknowledgment-controlled state handoff to coordinate workload balance, boundary communication, and vehicle handoff. Experiments on a 483 km2 Tianjin network with 1.5 million agents show that DiCoSim achieved a 14.49× strong-scaling speedup on 16 compute nodes while maintaining close agreement with centralized execution. For the fixed boundary-crossing evaluation cohort, the trajectory interruption rate was reduced to 0.06%. In addition, a single 72 h continuous high-load run achieved 99.96% availability. These results indicate that, under the tested Tianjin conditions, coordinated boundary management supports efficient million-agent microscopic simulation while maintaining vehicle-state continuity across partitions.