Calibration of Vehicular Traffic Simulation Models by Local Optimization

• URL: http://arxiv.org/abs/2502.11585v1
• Date: Mon, 17 Feb 2025 09:17:01 GMT
• Title: Calibration of Vehicular Traffic Simulation Models by Local Optimization
• Authors: Davide Andrea Guastella, Alejandro Morales-Hernàndez, Bruno Cornelis, Gianluca Bontempi,
• Abstract summary: Calibrating simulation models using traffic count data is challenging because of complexity of environment, lack of data, and uncertainties in traffic dynamics. This paper introduces a novel simulation-based traffic calibration technique. We assess the proposed technique on a model of Brussels, Belgium, using data from real traffic monitoring devices.
• Score: 43.942239814508106
• License:
• Abstract: Simulation is a valuable tool for traffic management experts to assist them in refining and improving transportation systems and anticipating the impact of possible changes in the infrastructure network before their actual implementation. Calibrating simulation models using traffic count data is challenging because of the complexity of the environment, the lack of data, and the uncertainties in traffic dynamics. This paper introduces 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 enables decentralizing the calibration task to reach near real-time performance, enabling the fostering of digital twins. Using only traffic count data makes the proposed method generic so that it can be applied in different traffic scenarios at various scales (from neighborhood to region). We assess the proposed technique on a model of Brussels, Belgium, using data from real traffic monitoring devices. The proposed method has been implemented using the open-source traffic simulator SUMO. Experimental results show that the traffic model calibrated using the proposed method is on average 16% more accurate than those obtained by the state-of-the-art methods, using the same dataset. We also make available the output traffic model obtained from real data.

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