# A Bayesian Additive Model for Understanding Public Transport Usage in Special Events

arXiv: https://arxiv.org/abs/1812.08755 · HTML: https://mlsanity.com/paper/1812.08755/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2018.

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

- [Exploring Large Language Models for Human Mobility Prediction under Public Events](https://mlsanity.com/paper/2311.17351/) (2023)
- [Gaussian Process Latent Class Choice Models](https://mlsanity.com/paper/2101.12252/) (2021)
- [Public Transit for Special Events: Ridership Prediction and Train Optimization](https://mlsanity.com/paper/2106.05359/) (2021)
- [Place with Intention: An Empirical Attendance Predictive Study of Expo 2025 Osaka, Kansai, Japan](https://mlsanity.com/paper/2601.14570/) (2026)

## Abstract (arXiv)

Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinate. The problem highly increases when several events happen concurrently. To solve these problems, costly processes, heavily reliant on manual search and personal experience, are usual practice in large cities like Singapore, London or Tokyo. This paper presents a Bayesian additive model with Gaussian process components that combines smart card records from public transport with context information about events that is continuously mined from the Web. We develop an efficient approximate inference algorithm using expectation propagation, which allows us to predict the total number of public transportation trips to the special event areas, thereby contributing to a more adaptive transportation system. Furthermore, for multiple concurrent event scenarios, the proposed algorithm is able to disaggregate gross trip counts into their most likely components related to specific events and routine behavior. Using real data from Singapore, we show that the presented model outperforms the best baseline model by up to 26% in R2 and also has explanatory power for its individual components.