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

## TL;DR

This paper introduces a Bayesian additive model with Gaussian processes that predicts public transport usage during special events by integrating smart card data and web-mined event information, improving prediction accuracy and disaggregation.

## Contribution

It presents a novel Bayesian additive model with efficient inference for predicting and disaggregating transport trips during concurrent events, leveraging real-time web data.

## Key findings

- Model outperforms baseline by up to 26% in R2
- Effective disaggregation of trip counts into event-specific components
- Demonstrated on real Singapore data

## Abstract

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.