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

Abstract

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.