Optimizing train movement has a great significance for railway traffic. In this paper, based on the optimal velocity car-following model, we propose a new simulation model for optimizing train movement in railway traffic. Here a kind of single-track railway is considered. Our aim is to reduce the energy consumption of train movement and ensure the train being on time by controlling the velocity curve of train movement. The simulation results indicate that the proposed model is effective for optimizing train movement. In addition, some major characteristics of train movement can be well captured. This method provides a new way to optimize train movement in railway traffic.
In this paper, a new model is constructed for the causation analysis of railway accident based on the complex network theory. In the model, the nodes are defined as various manifest or latent accident causal factors. By employing the complex network theory, especially its statistical indicators, the railway accident as well as its key causations can be analyzed from the overall perspective. As a case, the ＂7.23＂ China-Yongwen railway accident is illustrated based on this model. The results show that the inspection of signals and the checking of line conditions before trains run played an important role in this railway accident. In conclusion, the constructed model gives a theoretical clue for railway accident prediction and, hence, greatly reduces the occurrence of railway accidents.
The aim of this paper is to present a discrete event model-based approach to simulate train movement with the con- sidered energy-saving factor. We conduct extensive case studies to show the dynamic characteristics of the traffic flow and demonstrate the effectiveness of the proposed approach. The simulation results indicate that the proposed discrete event model-based simulation approach is suitable for characterizing the movements of a group of trains on a single railway line with less iterations and CPU time. Additionally, some other qualitative and quantitative characteristics are investigated. In particular, because of the cumulative influence from the previous trains, the following trains should be accelerated or braked frequently to control the headway distance, leading to more energy consumption.
Based on the optimal velocity car-following model, in this paper, we propose an improved model for simulating train movement in an urban railway in which the regenerative energy of a train is considered. Here a new additional term is introduced into a traditional car-following model. Our aim is to analyze and discuss the dynamic characteristics of the train movement when the regenerative energy is utilized by the electric locomotive. The simulation results indicate that the improved car-following model is suitable for simulating the train movement. Further, some qualitative relationships between regenerative energy and dynamic characteristics of a train are investigated, such as the measurement data of regenerative energy presents a power-law distribution. Our results are useful for optimizing the design and plan of urban railway systems.
