Calibration of Vehicular Traffic Simulation Models by Local Optimization

TL;DR

A novel stochastic simulation-based traffic calibration technique that performs local traffic calibration, allows calibrating simulated traffic in large-scale environments, and requires only the traffic count data, enabling the fostering of digital twins.

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.

Figures (24)

• Figure 1: Modeled scenario in the city of Brussels, Belgium. We partition the environment using square regions of 2000m$^2$ (Figure \ref{['fig:region_2000']}), 3500m$^2$ (Figure \ref{['fig:region_3500']}) and 5000m$^2$ (Figure \ref{['fig:region_5000']}).
• Figure 2: Average and standard deviation of the objective function (Equation \ref{['opt:calibration_problem']}) obtained by the proposed technique.
• Figure 3: Computational time (seconds) required to calibrate traffic using the proposed method.
• Figure 4: Computational time (seconds) required to simulate traffic using the proposed method.
• Figure 5: Total number of vehicles observed in all the regions and every time interval, in both the ground truth and the simulation. Figure \ref{['fig:traffic_vol_train']} and Figure \ref{['fig:traffic_vol_test']} show the total number of vehicles obtained on the training and testing sensors respectively.