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

arXiv: https://arxiv.org/abs/2302.03480 · HTML: https://mlsanity.com/paper/2302.03480/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 96 references that are in the map.
- First posted on arXiv 7 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [Batched Large-scale Bayesian Optimization in High-dimensional Spaces](https://mlsanity.com/paper/1706.01445/) (2017)
- [Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces](https://mlsanity.com/paper/1902.03229/) (2019)
- [Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables](https://mlsanity.com/paper/1910.01688/) (2019)
- [BOCK : Bayesian Optimization with Cylindrical Kernels](https://mlsanity.com/paper/1806.01619/) (2018)
- [Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models](https://mlsanity.com/paper/1501.03291/) (2015)
- [High Dimensional Bayesian Optimization Using Dropout](https://mlsanity.com/paper/1802.05400/) (2018)
- [ABC random forests for Bayesian parameter inference](https://mlsanity.com/paper/1605.05537/) (2016)
- [ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations](https://mlsanity.com/paper/1805.05480/) (2018)
- [Likelihood-Free Inference with Deep Gaussian Processes](https://mlsanity.com/paper/2006.10571/) (2020)
- [Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria](https://mlsanity.com/paper/1610.06462/) (2016)
- [Mobile phone records to feed activity-based travel demand models: MATSim for studying a cordon toll policy in Barcelona](https://mlsanity.com/paper/1803.06375/) (2018)
- [A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City](https://mlsanity.com/paper/2008.04762/) (2020)

## Built on by (most cited first, 4 of 4)

- [Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis](https://mlsanity.com/paper/2405.17468/) (2024)
- [Bias and Identifiability in the Bounded Confidence Model](https://mlsanity.com/paper/2506.11751/) (2025)
- [CALM: A Calibrated LLM Choice Network Framework for Activity-Based Traveler Simulation](https://mlsanity.com/paper/2609.22252/) (2026)
- [FLAT: Smoothing the Rugged Landscape for Learnable, Sample-Efficient Traffic Calibration](https://mlsanity.com/paper/2610.04337/) (2026)

## Abstract (arXiv)

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.