What can we learn from 9 years of ticketing data at a major transport hub? A structural time series decomposition
Document Type
Journal Article
Publication Date
2022
Subject Area
place - europe, place - urban, ridership - modelling, ridership - demand
Keywords
Structural time series models, decomposition, Kalman filter, public transportation data
Abstract
Mobility demand analysis is increasingly based on smart card data, that are generally aggregated into time series describing the volume of riders along time. These series present patterns resulting from multiple external factors. This paper investigates the problem of decomposing daily ridership data collected at a multimodal transportation hub. The analysis is based on structural time series models that decompose the series into unobserved components. The aim of the decomposition is to highlight the impact of long-term factors, such as trend or seasonality, and exogenous factors such as maintenance work or unanticipated events such as strikes or the COVID-19 health crisis. We focus our analysis on incoming flows of passengers to two transport lines known to be complementary in the Parisian public transport network. The available ridership data allows analysis over both long-term and short-term time horizons including significant events that have impacted people's mobility in the Paris region.
Rights
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Recommended Citation
de Nailly, P., Côme, E., Samé, A., Oukhellou, L., Ferriere, J., & Merad-Boudia, Y. (2022). What can we learn from 9 years of ticketing data at a major transport hub? A structural time series decomposition. Transportmetrica A: Transport Science, Vol. 18(3), pp. 1445-1469.
