Workdiamond2018

Bayesian Optimization for Transportation Simulators

20 citations0/0 files storedProcedia Computer Science

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Transportation Planning and Optimization Traffic Prediction and Management Techniques Gaussian Processes and Bayesian Inference

Abstract

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 we observe are the results of the emerging traveler’s behaviors and, in practice, we 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 we outline 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.

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References

2012Practical Bayesian Optimization of Machine Learning Algorithms 1995Bayesian Experimental Design: A Review 1975An efficient approach to solving the road network equilibrium traffic assignment problem 2014An enhanced SPSA algorithm for the calibration of Dynamic Traffic Assignment models 2010A gradient approximation approach for adjusting temporal origin–destination matrices 2014Input Warping for Bayesian Optimization of Non-stationary Functions 2008Statistical inference for time varying origin–destination matrices 2011Bayesian Demand Calibration for Dynamic Traffic Simulations

Cited by

2022Bayesian optimization techniques for high-dimensional simulation-based transportation problems 2023A Bayesian Optimization Approach for Calibrating Large-Scale Activity-Based Transport Models 2020Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation 2022Simulation-based analysis of second-best multimodal network capacity 2018Deep Reinforcement Learning for Dynamic Urban Transportation Problems

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