Research On Urban Rail Transit Scheduling For Predictable Large-scale Passenger Flow

ABSTRACT:With the rapid development of China’s economy, more and more events are held in big cities. During the events, most people choose to take their trips at the same period of time, which causes a large number of passengers’ aggregating in a very short amount of time. This produces great pressure on city transportation especially urban rail transit, and increases the risk rate. Therefore, to make trains operate safely, it is necessary to build a model of train schedule based on the demand of large passenger flow and get the reasonable train schedule. Based on the analysis above, this paper mainly reserches on the following aspects:(1) According to the specific operation situation of China’s urban rail transit, this paper defines the predictable large-scale passenger flow, describes its characteristics and analyzes its composition. What’s more, based on the existing forecasting methods, the paper takes the large-scale passenger flow which is caused by events for example, and discusses the forecasting difference between the predictable large passenger flow and the general flow. Then the paper assumes the passenger flow in peacetime is constant and uses the four-step method to forecast the increased passenger flow caused by the event on the subway.(2)The paper analyzes the theories about train scheduling of urban rail transit. It describes the relationship between train operation process and discrete event dynamic systems (DEDS). In addition, the paper introduces the advantage on using the max-algebra to build models of train schedule and the operational rule of max-algebra. Then on these bases, an open-loop nolinear model theory and a max-algebra linear model theory of train schedule are elaborated. The paper further studies the linear model of train schedule, which can provide the basis of building a closed-loop linear model of train schedule with the predicable large-scale passenger flow.(3)The paper builds the max-algebra closed-loop linear model of train schedule and designs an algorithm for the model. And it introduces the principles of building the model and analyzes the constraints of the model. Then the paper builds the max-algebra closed-loop linear model of train schedule. Besides that, the paper designs an algorithm with the max-algebra theory. It gets different input parameters through changing the coefficient matrices of this model, which can be got by adjusting the total turn-back time of trains. The paper determines the optimal input parameters according to the number of trains that passengers need, and gains the train schedule.(4)This paper takes a match held in a stadium of Beijing for example and analyzes these passengers who take the subway to watch the match. Then the paper forecasts the increased passengers of all the sections on Subway Line1. Next, the paper initializes the parameters, and gets the coefficient matrices and the optimal input parameters. Then it obtains the train schedule with the predictable large-scale passenger flow through the optimal input parameters. Finally, the paper analyzes the results of train schedule model.