Based on a car-following model, in this paper, we propose a new traffic model for simulating train movement in railway traffic. In the proposed model, some realistic characteristics of train movement are considered, such as the distance headway and the safety stopping distance. Using the proposed traffic model, we analyse the space-time diagram of traffic flow, the trajectory of train movement, etc. Simulation results demonstrate that the proposed model can be successfully used for simulating the train movement. Some complex phenomena can be reproduced, such as the complex acceleration and deceleration of trains and the propagation of train delay.
According to random walk, in this paper, we propose a new traffic model for scheduling trains on a railway network. In the proposed method, using some iteration rules for walkers, the departure and the arrival times of trains at each station are determined. We test the proposed method on an assumed railway network. The numerical simulations and the analytical results demonstrate that the proposed method provides an effective tool for scheduling trains. Some characteristic behaviours of train movement can be reproduced, such as train delay.
Based on deterministic NaSch model, we propose a new cellular automation model for simulating train movement. In the proposed model, the reaction time of driver/train equipment is considered. Our study is focused on the additional energy consumption arising by train delay around a traffic bottle （station）. The simulation results demonstrate that the proposed model is suitable for simulating the train movement under high speed condition. Further, we discuss the relationship between the additional energy consumption and some factors which affect the formation of train delay, such as the maximum speed of trains and the station dwell time etc.
In this paper, we propose an improved walk search strategy to solve the constrained shortest path problem. The proposed search strategy is a local search algorithm which explores a network by walker navigating through the network. In order to analyze and evaluate the proposed search strategy, we present the results of three computational studies in which the proposed search algorithm is tested. Moreover, we compare the proposed algorithm with the ant colony algorithm and k shortest paths algorithm. The analysis and comparison results demonstrate that the proposed algorithm is an effective tool for solving the constrained shortest path problem. It can not only be used to solve the optimization problem on a larger network, but also is superior to the ant colony algorithm in terms of the solution time and optimal paths.
LI KePing, GAO ZiYou , TANG Tao & YANG LiXing State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
Train control system plays a key role in railway traffic. Its function is to manage and control the train movement on railway networks. In our previous works, based on the cellular automata (CA) model, we proposed several models and algorithms for simulating the train movement under different control system conditions. However, these models are only suitable for some simple traffic conditions. Some basic fac- tors, which are important for train movement, are not considered. In this paper, we extend these models and algorithms and give a unified formula. Using the pro- posed method, we analyze and discuss the space-time diagram of railway traffic flow and the trajectories of the train movement. The numerical simulation and analytical results demonstrate that the unified CA model is an effective tool for simulating the train control system.
In this study, we improve L6vy walk model, and make it suitable for simulating the collective behaviours of humans. Here we show how rescuers find missing persons by collective cooperative search in a natural background. In the search process, the search strategy represents an optimal algorithm which is used to maximize the success rates for finding missingpcrsons. We simulate the rescuer＇s movement pattern, and find some basic laws governing the rescuer＇s cooperative search. For example, the probability that each rescuer finds missing persons shows a power law distribution.
