Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates...Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.展开更多
In large cities with heavily congested metro lines, unexpected disturbances often occur, which may cause severe delay of multiple trains, blockage of partial lines, and reduction of passenger service. Metro dispatcher...In large cities with heavily congested metro lines, unexpected disturbances often occur, which may cause severe delay of multiple trains, blockage of partial lines, and reduction of passenger service. Metro dispatchers have taken a practical strategy of rescheduling the timetable and adding several backup trains in storage tracks to alleviate waiting passengers from crowding the platforms and recover from such disruptions. In this study,we first develop a mixed integer programming model to determine the optimal train rescheduling plan with considerations of in-service and backup trains. The aim of train rescheduling is to frequently dispatch trains to evacuate delayed passengers after the disruption. Given the nonlinearity of the model, several linearization techniques are adapted to reformulate the model into an equivalent linear model that can be easily handled by the optimization software. Numerical experiments are implemented to verify the effectiveness of the proposed train rescheduling approach.展开更多
Purpose–This paper aims to propose a train timetable rescheduling(TTR)approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signal...Purpose–This paper aims to propose a train timetable rescheduling(TTR)approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.Design/methodology/approach–Firstly,a single-train trajectory optimization(STTO)model is constructed based on train dynamics and operating conditions.The train kinematics parameters,including acceleration,speed and time at each position,are calculated to predict the arrival times in the train timetable.A STTO algorithm is developed to optimize a single-train time-efficient driving strategy.Then,a TTR approach based on multi-train tracking optimization(TTR-MTTO)is proposed with mutual information.The constraints of temporary speed restriction(TSR)and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train.The multi-train trajectories at each position are optimized to generate a timeefficient train timetable.Findings–The numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF.The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay(TTD).As for the TSR scenario,the proposed TTR-MTTO can reduce TTD by 60.60%compared with the traditional TTR approach with dispatchers’experience.Moreover,TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.Originality/value–With the cooperative relationship and mutual information between train rescheduling and control,the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.展开更多
Train traffic rescheduling is a complicated and large-scaled combinatorial problem. According to the characteristics of China railway system and from the point of practical use, this paper introduces a rule-based trai...Train traffic rescheduling is a complicated and large-scaled combinatorial problem. According to the characteristics of China railway system and from the point of practical use, this paper introduces a rule-based train traffic reschedule interactive simulator. It can be used as a powerful training tool to train the dispatcher and to carry out experimental analysis. The production rules are used as the basic for describing the processes to be simulated. With the increase of rule, users can easily upgrade the simulator by adding their own rules.展开更多
Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway system.In such cases,train timetables need to be rescheduled.However,timely and efficient train timetable resc...Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway system.In such cases,train timetables need to be rescheduled.However,timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization efficiency.This paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based decisionmaking.Firstly,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway system.Then,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast convergence.Extensive experiments on various delay scenarios are conducted.The results demonstrate the effectiveness of the proposed method in comparison to other popular methods.展开更多
Urban rail transit (URT) disruptions present considerable challenges due to several factors: i) a high probability of occurrence, arising from facility failures, disasters, and vandalism;ii) substantial negative effec...Urban rail transit (URT) disruptions present considerable challenges due to several factors: i) a high probability of occurrence, arising from facility failures, disasters, and vandalism;ii) substantial negative effects, notably the delay of numerous passengers;iii) an escalating frequency, attributable to the gradual aging of facilities;and iv) severe penalties, including substantial fines for abnormal operation. This article systematically reviews URT disruption management literature from the past decade, categorizing it into pre-disruption and post-disruption measures. The pre-disruption research focuses on reducing the effects of disruptions through network analysis, passenger behavior analysis, resource allocation for protection and backup, and enhancing system resilience. Conversely, post-disruption research concentrates on restoring normal operations through train rescheduling and bus bridging services. The review reveals that while post-disruption strategies are thoroughly explored, pre-disruption research is predominantly analytical, with a scarcity of practical pre-emptive solutions. Moreover, future research should focus more on increasing the interchangeability of transport modes, reinforcing redundancy relationships between URT lines, and innovating post-disruption strategies.展开更多
基金supported by the China Fundamental Research Funds for the Central Universities(2022JBQY006)。
文摘Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.
基金supported by the National Natural Science Foundation of China (Nos. 61503020, 61403020 and U1434209)the Beijing Laboratory of Urban Rail Transit, the Beijing Key Laboratory of Urban Rail Transit Automation and Controlthe Major Program of Beijing Municipal Science & Technology Commission under Grant Z161100001016006
文摘In large cities with heavily congested metro lines, unexpected disturbances often occur, which may cause severe delay of multiple trains, blockage of partial lines, and reduction of passenger service. Metro dispatchers have taken a practical strategy of rescheduling the timetable and adding several backup trains in storage tracks to alleviate waiting passengers from crowding the platforms and recover from such disruptions. In this study,we first develop a mixed integer programming model to determine the optimal train rescheduling plan with considerations of in-service and backup trains. The aim of train rescheduling is to frequently dispatch trains to evacuate delayed passengers after the disruption. Given the nonlinearity of the model, several linearization techniques are adapted to reformulate the model into an equivalent linear model that can be easily handled by the optimization software. Numerical experiments are implemented to verify the effectiveness of the proposed train rescheduling approach.
基金This research was jointly supported by the National Natural Science Foundation of China[Grant 62203468]the Young Elite Scientist Sponsorship Program by China Association for Science and Technology(CAST)[Grant 2022QNRC001]+1 种基金the Technological Research and Development Program of China Railway Corporation Limited[Grant K2021X001]by the Foundation of China Academy of Railway Sciences Corporation Limited[Grant 2021YJ043].On behalf all authors,the corresponding author states that there is no conflict of interest.
文摘Purpose–This paper aims to propose a train timetable rescheduling(TTR)approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.Design/methodology/approach–Firstly,a single-train trajectory optimization(STTO)model is constructed based on train dynamics and operating conditions.The train kinematics parameters,including acceleration,speed and time at each position,are calculated to predict the arrival times in the train timetable.A STTO algorithm is developed to optimize a single-train time-efficient driving strategy.Then,a TTR approach based on multi-train tracking optimization(TTR-MTTO)is proposed with mutual information.The constraints of temporary speed restriction(TSR)and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train.The multi-train trajectories at each position are optimized to generate a timeefficient train timetable.Findings–The numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF.The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay(TTD).As for the TSR scenario,the proposed TTR-MTTO can reduce TTD by 60.60%compared with the traditional TTR approach with dispatchers’experience.Moreover,TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.Originality/value–With the cooperative relationship and mutual information between train rescheduling and control,the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.
文摘Train traffic rescheduling is a complicated and large-scaled combinatorial problem. According to the characteristics of China railway system and from the point of practical use, this paper introduces a rule-based train traffic reschedule interactive simulator. It can be used as a powerful training tool to train the dispatcher and to carry out experimental analysis. The production rules are used as the basic for describing the processes to be simulated. With the increase of rule, users can easily upgrade the simulator by adding their own rules.
基金supported partially by the National Natural Science Foundation of China(61790573,61790575)the Center of National Railway Intelligent Transportation System Engineering and Technology(RITS2019KF03)+3 种基金China Academy of Railway Sciences Corporation LimitedChina Railway Project(N2019G020)China Railway Project(L2022X002)the Key Project of Science and Technology Research Plan of China Academy of Railway Sciences Group Co.Ltd.(2022YJ326)。
文摘Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway system.In such cases,train timetables need to be rescheduled.However,timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization efficiency.This paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based decisionmaking.Firstly,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway system.Then,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast convergence.Extensive experiments on various delay scenarios are conducted.The results demonstrate the effectiveness of the proposed method in comparison to other popular methods.
基金supported by the National Natural Science Foundation of China(Grant Nos.72122014 and 72061127003)the Sustainable Urban Future Laboratory of ZJU-UIUC Institute.
文摘Urban rail transit (URT) disruptions present considerable challenges due to several factors: i) a high probability of occurrence, arising from facility failures, disasters, and vandalism;ii) substantial negative effects, notably the delay of numerous passengers;iii) an escalating frequency, attributable to the gradual aging of facilities;and iv) severe penalties, including substantial fines for abnormal operation. This article systematically reviews URT disruption management literature from the past decade, categorizing it into pre-disruption and post-disruption measures. The pre-disruption research focuses on reducing the effects of disruptions through network analysis, passenger behavior analysis, resource allocation for protection and backup, and enhancing system resilience. Conversely, post-disruption research concentrates on restoring normal operations through train rescheduling and bus bridging services. The review reveals that while post-disruption strategies are thoroughly explored, pre-disruption research is predominantly analytical, with a scarcity of practical pre-emptive solutions. Moreover, future research should focus more on increasing the interchangeability of transport modes, reinforcing redundancy relationships between URT lines, and innovating post-disruption strategies.
