Abstract:
Simulation is a valuable tool to assist traffic management experts in refining and improving transportation systems and anticipating the impact of possible changes in the infrastructure network before the actual implementation. Calibrating simulation models using traffic counts data is challenging because of the complexity of the environment, the lack of data, and the uncertainties in traffic dynamics. I present a novel stochastic simulation-based traffic calibration technique. The novelty of the proposed method is: (i) it performs local traffic calibration, (ii) it allows calibrating simulated traffic in large-scale environments, (iii) it requires only the traffic count data. The local approach makes it possible to decentralize the calibration task to reach near real-time performance, enabling fostering digital twins. I showcase the benefits of the proposed method in a model of Brussels, Belgium, using real traffic counts, and compare the results to those obtained by standard optimization technique.