# Bayesian Calibration for Activity Based Models

arXiv: https://arxiv.org/abs/2203.04414 · HTML: https://mlsanity.com/paper/2203.04414/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 112 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [Scalable Bayesian Optimization Using Deep Neural Networks](https://mlsanity.com/paper/1502.05700/) (2015)
- [Dimensionality-Driven Learning with Noisy Labels](https://mlsanity.com/paper/1806.02612/) (2018)
- [The Parallel Knowledge Gradient Method for Batch Bayesian Optimization](https://mlsanity.com/paper/1606.04414/) (2016)
- [Input Warping for Bayesian Optimization of Non-stationary Functions](https://mlsanity.com/paper/1402.0929/) (2014)
- [Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration](https://mlsanity.com/paper/1304.5350/) (2013)
- [Parallel Bayesian Global Optimization of Expensive Functions](https://mlsanity.com/paper/1602.05149/) (2016)
- [Practical heteroskedastic Gaussian process modeling for large simulation experiments](https://mlsanity.com/paper/1611.05902/) (2016)
- [Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions](https://mlsanity.com/paper/1511.07130/) (2015)

## Built on by (most cited first, 3 of 3)

- [Generative Modeling: A Review](https://mlsanity.com/paper/2501.05458/) (2024)
- [Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates](https://mlsanity.com/paper/2602.21408/) (2026)
- [CALM: A Calibrated LLM Choice Network Framework for Activity-Based Traveler Simulation](https://mlsanity.com/paper/2609.22252/) (2026)

## Abstract (arXiv)

We consider the problem of calibration and uncertainty analysis for activity-based transportation simulators. Activity-Based Models (ABMs) rely on statistical modeling of individual travelers' behavior to predict higher-order travel patterns in metropolitan areas. Input parameters are typically estimated from traveler surveys using maximum likelihood. We develop an approach that uses a Gaussian Process emulator to calibrate those parameters using traffic flow data. Our approach extends traditional emulators to handle the high-dimensional and non-stationary nature of transportation simulators. We introduce a deep learning dimensionality reduction model that is jointly estimated with Gaussin Process model to approximate the simulator. We demonstrate the methodology using several simulated examples as well as by calibrating key parameters of the Bloomington, Illinois model.