In this study, we focus on improving the efficiency of an urban rail transit line under the circumstance of spatially unbalanced passenger demand. A flexible short-turning strategy is integrated into the train scheduling problem, aiming to obtain a train timetable and the corresponding circulation plan adapted to a time-dependent passenger demand. First, we formulate the dynamic passenger demand-oriented train scheduling problem as a multi-commodity network flow optimization model in a two-layer space-time network. The proposed model is then decomposed into train scheduling and passenger assignment sub-problems by relaxing the coupling constraint. Therefore, an optimal solution of the original model can be obtained by iteratively solving two easy-to-solve sub-problems in a Lagrangian relaxation solution framework. The effectiveness of the model is evaluated using a series of simple experiments and a real-world case study based on the Beijing Yizhuang Line.

Assessing and prioritizing the duration time and effects of traffic incidents on major roads present significant challenges for road network managers. This study examines the effect of numerous factors associated with various types of incidents on their duration and proposes an incident duration prediction model. Several parametric accelerated failure time hazard-based models were examined, including Weibull, log-logistic, log-normal, and generalized gamma, as well as all models with gamma heterogeneity and flexible parametric hazard-based models with freedom ranging from one to ten, by analyzing a traffic incident dataset obtained from the Incident Reporting and Dispatching System in Beijing in 2008. Results show that different factors significantly affect different incident time phases, whose best distributions were diverse. Given the best hazard-based models of each incident time phase, the prediction result can be reasonable for most incidents. The results of this study can aid traffic incident management agencies not only in implementing strategies that would reduce incident duration, and thus reduce congestion, secondary incidents, and the associated human and economic losses, but also in effectively predicting incident duration time.