A Bayesian Optimization approach for calibrating large-scale activity-based transport models

TL;DR

This paper introduces a Bayesian Optimization method with an improved Random Forest surrogate to efficiently calibrate large-scale activity-based transport models, demonstrated on a case study with 477 parameters.

Contribution

It presents a novel Bayesian Optimization approach tailored for calibrating complex, high-dimensional activity-based transportation models.

Findings

Achieved 4% error in total trip count

Attained 15.92 vehicle error in OD matrix

Demonstrated effectiveness on a real city case study

Abstract

The use of Agent-Based and Activity-Based modeling in transportation is rising due to the capability of addressing complex applications such as disruptive trends (e.g., remote working and automation) or the design and assessment of disaggregated management strategies. Still, the broad adoption of large-scale disaggregate models is not materializing due to the inherently high complexity and computational needs. Activity-based models focused on behavioral theory, for example, may involve hundreds of parameters that need to be calibrated to match the detailed socio-economical characteristics of the population for any case study. This paper tackles this issue by proposing a novel Bayesian Optimization approach incorporating a surrogate model in the form of an improved Random Forest, designed to automate the calibration process of the behavioral parameters. The proposed method is tested on a case study for the city of Tallinn, Estonia, where the model to be calibrated consists of 477 behavioral parameters, using the SimMobility MT software. Satisfactory performance is achieved in the major indicators defined for the calibration process: the error for the overall number of trips is equal to 4% and the average error in the OD matrix is 15.92 vehicles per day.