01697513

Component

Mobility dynamics in urban transportation systems is governed by a large number of travelers that act according to their utilities, preferences and biases. The mobility patterns that the authors observe are the results of the emerging traveler’s behaviors and, in practice, the authors develop models that represents mobility patterns and their resulting traffic flows. Models, however, can only approximate the processes they represent and often times do not reproduce exact matches to the true system’s observed data. Systematic adjustments, or calibrations, to the model and its input variables may be required to align the associated outputs more closely with their true values. In this paper the authors out line a mathematical framework that allows the calibration for parameters of urban transportation models through a distributed, Gaussian Process Bayesian regression with active learning methods and demonstrate using a ground transportation simulation model.

This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.

19-02479

English

11p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References

Bayes' theorem; Calibration; Mobility; Optimization; Regression analysis; Traffic simulation; Travel behavior; Travel demand; Urban transportation

Planning and Forecasting; Transportation (General)

Transportation Research Board Annual Meeting 2019 Paper #19-02479

TRIS, TRB, ATRI

Dec 7 2018 9:29AM