Urban rail transit train operation adjustment scheme optimization method and system

Abstract

The invention relates to an urban rail transit train operation adjustment scheme optimization method and system. The method comprises the following steps: 1, constructing a rail transit train operation element constraint; 2, carding common train operation adjustment strategies, and abstractly expressing the common train operation adjustment strategies as mathematical formulas; 3, establishing a train operation adjustment scheme optimization model; and 4, solving the model by combining a greedy algorithm with a train timetable calculation method to obtain a train operation adjustment scheme. According to the method, urban rail transit train operation process elements and common operation adjustment strategies are abstracted into mathematical models, and compared with a mode of formulating a scheme mainly according to artificial experience in the current reality, the operation adjustment scheme obtained through a quantitative optimization mode is more scientific and reasonable. The method focuses on the typical'line traffic capacity decline 'scene in the actual operation process, the train operation constraint is considered in detail, and the applicability of the adjustment scheme is fully ensured. And the high timeliness requirement of train operation adjustment under emergencies can be met.

Description

technical field [0001] The invention relates to the field of railway traffic management, in particular to a method and system for optimizing an urban rail transit train operation adjustment scheme. Background technique [0002] With the development of my country's urban rail transit, while the network scale and passenger flow are growing rapidly, the operating pressure and load of urban rail transit are gradually increasing. In addition, the urban rail transit system is a complex whole integrating multiple systems such as vehicles, signals, and power supply. Any unexpected situation in any link will affect the operating efficiency of the line or even the network. Therefore, after an emergency disrupts the original operation plan, it is necessary to quickly formulate a corresponding operation adjustment plan to reduce train operation and passenger travel delays, and restore the original operation plan as soon as possible after the emergency is over. [0003] In the actual op...

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Problems solved by technology

[0011](2) Most of the relevant researches are based on the background of intercity railways. Since the length of the lines is generally long, the turnaround time of trains is long, and the interval between trains is relatively large, this part The scheme usually does not consider the two-way train bottom turnover problem, but the urban rail transit train turnover is fast and the running interval is small, ignoring the adjustment of the car bottom turnover plan will reduce the feasibility of the operation adjustment plan

[0012](3) Compared with the intercity railway system, urban rail transit lines have different train and signal system standards, the connectivity between lines is poor, and the lines inside the stations Therefore, the adjustment strategies commonly used in the existing technical solutions such as "waiting at the station" and "roundabout operation" are less applicable to urban rail transit systems, and relevant theories and methods cannot directly provide effective guidance

[0013](4) The problem of train operation adjustment under emergencies has high timeliness requirements for plan formulation

Some existing technical solutions use commercial solvers to solve them, but most commercial solvers can only handle linear programming models. When the model contains complex nonlinear constraints, the solution efficiency is low, and the efficiency of commercial solvers has relatively little effect on model input parameters. high sensitivity

In addition, currently few theoretical solutions are effectively integrated into easy-to-operate software systems, so the practicability of technical solutions needs to be improved

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