At present, the existing dynamic OD estimation methods in an urban rail transit network still need to be improved in the factors of the time-dependent characteristics of the system and the estimation accuracy of the results. This study focuses on predicting the dynamic OD demand for a time of period in the future for an urban rail transit system. We propose a nonlinear programming model to predict the dynamic OD matrix based on historic automatic fare collection (AFC) data. This model assigns the passenger flow to the hierarchical flow network, which can be calibrated by backpropagation of the first-order gradients and reassignment of the passenger flow with the updated weights between different layers. The proposed model can predict the time-varying OD matrix, the number of passengers departing at each time, and the travel time spent by passengers, of which the results are shown in the case study. Finally, the results indicate that the proposed model can effectively obtain a relatively accurate estimation result. The proposed model can integrate more traffic characteristics than traditional methods and provides an effective and hierarchical passenger flow estimation framework. This study can provide a rich set of passenger demand for advanced transit planning and management applications, for instance, passenger flow control, adaptive travel demand management, and real-time train scheduling.

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Adverse drug event (ADE) is a significant challenge in clinical practice. Many ADEs have not been identified timely after the approval of the corresponding drugs. Despite the use of drug similarity network demonstrates early success on improving ADE detection, false discovery rate (FDR) control remains unclear in its application. Additionally, performance of early ADE detection has not been explicitly investigated under the time-to-event framework. In this manuscript, we propose to use the drug similarity based posterior probability of null hypothesis for early ADE detection. The proposed approach is also able to control FDR for monitoring a large number of ADEs of multiple drugs. The proposed approach outperforms existing approaches on mining labeled ADEs in the US FDA Adverse Event Reporting System (FAERS) data, especially in the first few years after the drug initial reporting time. Additionally, the proposed approach is able to identify more labeled ADEs and has significantly lower time to ADE detection. In simulation study, the proposed approach demonstrates proper FDR control, as well as has better true positive rate and an excellent true negative rate. In our exemplified FAERS analysis, the proposed approach detects new ADE signals and identifies ADE signals in a timelier fashion than existing approach. In conclusion, the proposed approach is able to both reduce the time and improve the FDR control for ADE detection.