Short-Term Urban Rail Passenger Flow Forecasting: A Dynamic Bayesian Network Approach

We propose a dynamic Bayesian network approach to forecast the short-term passenger flows of the urban rail network of Paris. This approach can deal with the incompleteness of the data caused by failures or lack of collection systems. The structure of the model is based on the causal relationships between the adjacent flows and is designed to take into account the transport service. To reduce the number of arcs and find the maximum likelihood estimate of the parameters, we perform the structural expectation-maximization (EM) algorithm. Then short-term forecasting is conducted by inference, using the bootstrap filter. An experiment is carried out on an entire metro line, using ticket validation, count and transport service data. Overall, the forecasting results outperform historical average and last observation carried forward (LOCF). They illustrate the potential of the approach, as well as the key role of the transport service.

I. Introduction

RATP is the main public transport operator in the Paris region. It operates all 16 metro lines, sections of 2 RER (commuter rail) lines, 8 tramway lines and more than 350 bus lines. Currently, it uses several tools for passenger flow modeling, whose purpose is to assess the long-term effects of infrastructure or transport policy changes. However, these models are not designed for short-term forecasting and cannot take into account the impacts of unanticipated or non-recurrent events (e.g., service disruptions, unplanned closures of stations, crowd-attracting events). Furthermore, the diversity of available data is still largely untapped. Each service generally works with its own sources and thus only has a partial view of the mobility within the network.