Research On Train Rescheduling In Urban Rail Transit Networks

During the process of daily operation,urban rail transit systems are more or less unavoidably subject to unexpected emergencies disturbing the train operation,which influences the level of service seriously.Currently,with regard to the train rescheduling under emergencies,single-line dispatching is the mainstream in existing methods.Under the condition of networking operation,the relation between lines is stronger influencing each other,and single-line dispatching can't satisfying the new requirements.As a result,from the view of partial and complete network respectively,several train rescheduling models are proposed considering passengers' transfer behaviors based on coordinated dispatching theory,and corresponding solution algorithms are designed based on the model structure to solve the large-scale,real-world cases within limited time,as well as validating the effectiveness of the proposed rescheduling methods.The detailed research contents include:(1)Train rescheduling considering connecting lines.With regard to the train rescheduling problem on a delayed line and its connecting lines during the off-peak period,focusing on transfer passengers from the delayed line,by describing the connection relationship between trains on the delayed line and trains on connecting lines at transfer stations,a train rescheduling model is proposed to minimize the transfer waiting time for all transfer passengers,meanwhile to minimize the deviation between the rescheduled timetable and the planned one.The bi-objective model is reformulated by the e-constraint method,then it can be programed in CPLEX and solved by branch and cut algorithm efficiently when tackling a large-scale,real-world case.Finally,a case study is conducted based on Line 7 of the Beijing subway and its connecting lines,a set of approximate Pareto optimal solutions are obtained,which shows the effectiveness and practicability of the proposed method.(2)Networking last train rescheduling.First,from the view of network accessibility,a last-train rescheduling model is proposed to maximize the number of successful transfer passengers,meanwhile to minimize the deviation between the rescheduled timetable and the planned one.Second,considering that the operation time of each line's last train varies considerably,transfer passengers are divided into two types:"Last to Last" and "Last to Un-last".Then,another last-train rescheduling model is developed to maximize the number of successful transfer passengers,meanwhile to minimize the average transfer waiting time for all "Last to Last" transfer passengers.A branch and cut algorithm and an efficient genetic algorithm with elite strategy are used to solve two models respectively when dealing with large-scale,real-world cases.Finally,some numerical experiments are carried out based on the Beijing subway network,and two models can generate rescheduled plan efficiently to improve the network accessibility.(3)Networking train rescheduling during the end-of-service period.First,the conception of the end-of-service period is proposed and the complexity of the train rescheduling problem during the end-of-service period is analyzed.Second,focusing on all transfer passengers during the end-of-service period,a train rescheduling model is proposed to minimize the transfer waiting time for all transfer passengers,meanwhile to minimize the deviation between the rescheduled timetable and the planned one.For transfer passengers who miss the last train of connecting line,penalty time is used to represent their transfer waiting time,then the model is able to reduce the number of failed transfer passengers.The model is reformulated using the e-constraint method so that it can be programed in CPLEX and solved by branch and cut algorithm efficiently when tackling a large-scale,real-world case.Finally,a case study based on the Beijing subway network is conducted,and results show that this method can reduce passenger transfer waiting time as well as the number of failed transfer passengers to quickly generate a rescheduled plan with better transfer efficiency and network accessibility.