Naïve Bayes-Based Transition Model for Short-Term Metro Passenger Flow Prediction under Planned Events

Abstract

Short-term passenger flow prediction under planned events is important to reduce passenger delay and ensure operational safety in metro systems. However, most studies make predictions under normal conditions. The study proposes a naïve Bayes transition model for short-term passenger flow prediction under planned events. The target prediction scenario identification is modeled as a binary classification problem using naïve Bayes. The sub-models are developed using gradient boosting decision tree (GBDT) and deep learning (DL) models for normal and planned event scenarios with predictor variables tailored to different passenger demand patterns. The sub-predictor from GBDT or DL is selected based on the inferred prediction scenario. The case study uses automatic fare collection (AFC) data of Shanghai and Hong Kong metro systems. The results show that the proposed model outperforms other representative individual and fusion models. The results also highlight the effectiveness of the predictive transition mechanism between the normal and planned events and also the event information representation.

Keywords

data and data science artificial intelligence Bayesian analysis machine learning (artificial intelligence)neural networks public transportation transformative trends in transit data big data smart card data

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