Meta-heuristic aggregate calibration of transport models exploiting data collected in mobility

The wide diffusion of data collected in mobility led to an unprecedented amount of information about people's mobility behavior. While on one hand the availability of big data from multiple sources enables to calibrate complex models with a high number of parameters, on the other hand, the dimension of the problem increases, and computational efficiency becomes an important issue. The paper presents a general methodology for the aggregate calibration of transport system models that exploits data collected in mobility jointly with other data sources within a multi-step optimization procedure based on metaheuristic algorithms. The methodology is applied to two real large-scale case studies in two different contexts. The first concerns the aggregate calibration updating a national strategic 4-step demand model in use in a big European Country; the second deals with the calibration of link and node performance functions implemented in a traffic network model of a town of around 3 million inhabitants. The results demonstrate the effectiveness of the aggregate calibration methodology in significantly improving earlier models' estimations. The results also highlight that the errors are in the same order of magnitude as the intrinsic variation of the data collected in the field.