| The current metro timetable and pre-stored train control schemes have shortcomings when facing with passenger flow fluctuation,such as the decline of service level and the failure to consider train energy consumption.From the prospective of passenger service level and operational cost,the online optimization method of train timetable and control schemes considering passenger flow fluctuation and energy saving is studied in this paper.Firstly,based on the idea of rolling optimization,a metro train control framework considering real-time passenger flow is formulated,and the online generation and execution process of train timetable and control schemes are introduced.Secondly,based on passenger flow fluctuation,an online optimization method of train timetable is proposed to minimize total waiting time of passengers.Then,considering the utilization of regenerative braking energy(RBE),the online optimization of cooperative train control for energy saving is studied to minimize train net energy.Finally,applicability of the proposed method is discussed in case studies based on the actual line.Main contents and conclusions are as follows:(1)In reality,the randomness and fluctuation of practical passenger flow make it difficult to achieve the expected service level of train timetable and energy-saving effect of the pre-stored train control schemes.Based on the analysis of the current metro operation mode,an online optimization method of train timetable and control schemes is proposed considering passenger flow fluctuation and energy saving.Firstly,based on CBTC system,a metro train control framework with the consideration of real-time passenger flow is formulated,whose components and mechanisms are introduced.On this basis,considering actual passenger flow fluctuation,the current offline generation of timetable and pre-calculation of train control schemes are transformed into a staged online optimization process with the help of rolling optimization idea,so as to reduce the net energy and operational cost on the premise of ensuring passenger service level.(2)Based on the above train control framework and rolling optimization idea.An online optimization model of timetable parameters is formulated,which takes train arrivals as the segmentation points of the rolling horizon.The total waiting time of passengers in the horizon is calculated according to the actual passenger flow,and timetable parameters are online generated to effectively deal with the randomness and fluctuation of passenger arrivals.Three heuristic algorithms including GASA,PSO and DEA are used to solve the model.Case studies show that compared with the original scheme,timetable generated by the proposed rolling optimization model can effectively save passenger waiting time up to 4.72%.In addition,the effect of three algorithms are compared,where GASA and DEA have the best performance in computation time and optimization rate respectively,while PSO performs best overall.(3)Based on the above train control framework and the optimized train timetable,an online optimization method of cooperative train control for energy saving is proposed.On the basis of four-phase control strategy,an energy-efficient train control model with the aim of minimizing the net energy is formulated.By flexibly adjusting train control regimes and adding multiple tractions during train operation,the net energy can be reduced.Brute Force algorithm with an enhanced searching strategy is developed to solve the model.Case studies show that the proposed train control method can save energy in different scenarios.For single train operation under different line ramp configurations,energy consumption can be reduced by 3.12% on average.In the case of multi-train operation with different headways,the utilization of RBE can be increased by 13.32% on average,and the net energy saving rate reaches 5.46%.Besides,the proposed algorithm can generate train control schemes in a short time,which meets the requirement of real-time optimization. |
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