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.

出行需求分析越来越多地基于智能卡数据，这些数据通常汇总为时间序列，描述随时间推移的乘客数量。这些系列呈现出由多种外部因素导致的模式。本文研究了分解在多式联运枢纽收集的每日客流量数据的问题。该分析基于将序列分解为未观察到的组件的结构化时间序列模型。分解的目的是突出长期因素的影响，例如趋势或季节性，以及外部因素，例如维护工作或意外事件，例如罢工或 COVID-19 健康危机。我们将分析重点放在前往两条已知在巴黎公共交通网络中互补的交通线路的乘客流量上。