Integrated Optimization Of Train Timetable,Rolling Stock Circulation Plan And Short-Turning Strategy For An Urban Rail Transit Line

With the continuous growth of urban rail transit network,the spatial coverage of urban rail transit is expanded,thereby enhancing the network accessibility.In the traditional organization way,the departure interval is uniform and the routing is single.In order to meet the large passenger demand in some sections,more rolling stocks need to be operated to provide more services,but it causes the transport capacity waste in smaller demand sections.The short-turning strategy is an organization way to balance the distribution of passenger demand in space and time,aiming to meet the passenger demand in different sections.However,the passenger demand is time-varying in peak hours and if the headway is still uniform,it results in a poor match between transport capacity and passenger demand,which increases the waiting time of passengers.As a result,how to design a better train timetable and rolling stock circulation plan becomes a key to solve the unbalanced distribution of passenger demand in space and time。Based on the careful analysis of existing literature,this thesis first integrates train timetable and rolling stock circulation plan under the dynamic passenger demand,with the objective to balance the passenger demand distribution.Next,an optimization model of integrating short-turning strategy,train timetable,and rolling stock circulation plan is proposed.In the thesis,the main contents are as follows:(1)In order to solve the unbalanced distribution of passenger demand in space and time,this thesis presents an integrated optimization method for train timetable and rolling stock circulation plan under the dynamic passenger demand,which better matches the passenger demand and guarantees that each service can be undertaken by a rolling stock.A nonlinear mixed integer programming model is constructed with the objective of minimizing the total remaining passengers at platforms and total operating costs.Specifically,the headway,train capacity,train connections and the number of rolling stocks are simultaneously considered.Finally,based on the linearization techniques of time-label and big M methods,the model is reconstructed into a mixed integer linear programming model,which can be solved by optimization solvers,e.g.,Cplex and Gurobi.(2)Based on the short-turning strategy,this thesis presents a collaborative optimization method for train timetable,rolling stock circulation plan.With the limited rolling stocks and infrastructure,the model can properly allocate the transportation capacity,to increase the transportation capacity in lager demand sections,and reduce the transportation capacity in smaller demand sections,while achieving overall optimization.A nonlinear mixed integer programming model is established with the objective of minimizing the total waiting time of passengers at platforms.Specifically,the routing selections,headway,train capacity,train connections and the number of rolling stocks are all considered.Based on the model structure,a hybrid algorithm is designed to solve the proposed model.(3)This thesis takes the Beijing Yizhuang line and line 6 as examples to verify the two proposed optimization models.Based on the actual data of the Beijing Metro Yizhuang Line,the effectiveness of the collaborative optimization model of the train schedule and the rolling stock circulation plan under the time-varying passenger flow demand is verified.Compared with the actual timetable,the optimized train timetable can reduce the number of stranded passengers on platforms by 64.72%,and reduce the waiting time of passengers by 30.77%,while reducing the operating cost by 6.67%.This shows that the optimized train timetable can better match the passenger demand,and meanwhile reduce operating costs.Based on the actual data of Beijing Metro Line 6,the effectiveness of the collaborative optimization model of the train schedule and the undercarriage utilization plan based on the large and small crossing scheme is verified.Compared with the actual timetable,the optimized train timetable can reduce the number of stranded passengers by 44.69% and reduce the waiting time of passengers on platforms by 29.02%,which shows that the optimized model can better allocate the transport capacity to match the passenger demand in different sections,with the limited resources and infrastructure.34 figures,18 tables and 87 reference literatures exist in the thesis.