Research For The Train Operation Regulation Of CBTC System Based On Ant Colony Optimization Algorithm

The urban rail transportation is taking more and more important role on releasing the urban traffic pressure. There are lots of advantages than other vehicles such as heavy capacity, fast speed, safety and less disturb. Generally, the train operation complies with the arranged timetable strictly. While, for some random reasons, it is hard to avoid deflection from the contemplated schedule. Therefore, We must adjust the train running so as to restore the orderly operation based on the contemplated schedule, especially for the train of CBTC system. The moving block technology based the CBTC system make the train operation regulation much more difficult, because of shorter headway of trains and higher traffic density. So the research on the train operation regulation is very necessary and important.The paper completes the following work:1. Firstly, We put forward an single-objective and multi-constraint model pertinently through an in-depth and careful analysis on the characteristics of train operation adjustment itself, urban railway traffic and CBTC system.2. Then, ant colony optimization algorithm (ACO) is selected to get solution of the model considering the combinatorial optimization of train regulation. The essay clarifies the application process of ACO in operation adjustment, including establishing a graph to approach the solutions, describing and processing the optimization objectives and constraints in algorithm.3. In the end, the simulations are carried out under the train slight delay and delays propagation train serious delay and train group delay. We can see that the deviation from the plan schedules has been greatly improved after adjusting the train operation by the analysis of simulation results. It proves the algorithm of the train operation regulation based on ACO is effective and feasible.