Research on the Operation Adjustment Strategy of Cross-line Trains on Urban Rail that Adapted to Dynamic Passenger Flow

In order to reduce the travelling time and improve travelling efficiency which are in great demand in the city especially in the busy metropolis, the operation adjustment strategy of cross-line trains on urban transit was studied. On the basis of considering specific condition of line and dynamic characteristic of passenger flow, a cross-line train operation adjustment model adapted to dynamic passenger flow was established, the operation adjustment strategy was proposed by using Coyote Optimization Algorithm(COA) to optimize the tracking interval between adjacent trains. The adjustment model of train operation was realized through two parts. In the first part, under the condition of passenger flow during normal hours, the tracking interval of adjacent trains was optimized with COA, and the operation strategy of trains was obtained. In the second part, under the condition of passenger flow during peak hours, the tracking interval and stop time was adjusted in the same time, and the operation strategy of trains was obtained. Based on the real track data and vehicle parameters of a line of a city, the optimization method was simulated and verified. Simulation result shows that after optimization, the travelling time of passengers during normal hours reduces by 3.11% in the first part, and the total waiting time of passengers during peak hours reduces by 32.50%, the total travelling time of passengers reduces by 3.93% in the second part.

Automated summary

In this study, an operation adjustment strategy for cross-line trains on urban transit was proposed to reduce travelling time and improve travelling efficiency. By utilizing an optimization algorithm, the tracking interval between adjacent trains was optimized based on specific conditions and dynamic characteristics of passenger flow. Simulation results showed that the optimized operation strategy significantly reduced the travelling time and waiting time for passengers under different passenger flow conditions.