Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression
提出贝叶斯马尔可夫机制转换向量自回归模型，联合预测公交行程时间与乘客占用率及其不确定性，利用真实数据验证了其在点估计和不确定性量化上优于贝叶斯高斯混合模型。
Accurate forecasting of bus travel time and passenger occupancy with uncertainty is essential for both travelers and transit agencies/operators. However, existing approaches to forecasting bus travel time and passenger occupancy mainly rely on deterministic models, providing only point estimates. In this paper, we develop a Bayesian Markov regime-switching vector autoregressive model to jointly forecast both bus travel time and passenger occupancy with uncertainty. The proposed approach naturally captures the intricate interactions among adjacent buses and adapts to the multimodality and skewness of real-world bus travel time and passenger occupancy observations. We develop an efficient Markov chain Monte Carlo (MCMC) sampling algorithm to approximate the resultant joint posterior distribution of the parameter vector. With this framework, the estimation of downstream bus travel time and passenger occupancy is transformed into a multivariate time series forecasting problem conditional on partially observed outcomes. Experimental validation using real-world data demonstrates the superiority of our proposed model in terms of both predictive means and uncertainty quantification compared to the Bayesian Gaussian mixture model. • A Bayesian probabilistic model for jointly forecasting travel time and occupancy. • Interactions among adjacent bus runs are captured with a Markov regime-switching model. • Forecasting for downstream links is transformed into a multivariate time series forecasting conditional on partially observed data. • The model achieves superior performance in both point estimation and uncertainty quantification.