Improving Synchronization in High-Speed Railway and Air Intermodality: Integrated Train Timetable Rescheduling and Passenger Flow Forecasting

Tan, Yuyan; Li, Yibo; Wang, Ruxin; Mi, Xiwei; Li, Ya-Xuan; Zheng, Hao; Yu, Kun; Wei, Yan

doi:10.1109/tits.2021.3137410

Cited by 18 publications

(3 citation statements)

References 30 publications

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“…Punctuality development and delay explanation factors on Norwegian railways were analyzed, offering a historical perspective on delay trends and contributing to the understanding of delay management strategies [ 25 ]. Focusing on determining operations affected by delay in predictive train timetables, a multistage decision optimization approach for train timetable rescheduling under uncertain disruptions in a high-speed railway network was proposed, which has significant implications for enhancing the resilience of railway operations and reducing the severity of delays during unexpected events [ 26 ]. These studies collectively emphasize the importance of robust delay measurement and management strategies, showcasing various approaches, including data-driven methods and optimization techniques, to better understand, predict, and mitigate the impact of train delays on rail networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring high-speed train delay severity: Static and dynamic analysis

Li,

Wen,

Yang

et al. 2024

PLoS ONE

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This paper focuses on optimizing the management of delayed trains in operational scenarios by scientifically categorizing train delay levels. It employs static and dynamic models grounded in real-world train delay data from high-speed railways. This classification aids dispatchers in swiftly identifying and predicting delay extents, thus enhancing mitigation strategies’ efficiency. Key indicators, encompassing initial delay duration, station impacts, average station delay, delayed trains’ cascading effects, and average delay per affected train, inform the classification. Applying the K-means clustering algorithm to standardized delay indicators yields an optimized categorization of delayed trains into four levels, reflecting varying risk levels. This static classification offers a comprehensive overview of delay dynamics. Furthermore, utilizing Markov chains, the study delves into sequential dynamic analyses, accounting for China’s railway context and specifically addressing fluctuations during the Spring Festival travel rush. This research, combining static and dynamic approaches, provides valuable insights for bolstering railway operational efficiency and resilience amidst diverse delay scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

Measuring high-speed train delay severity: Static and dynamic analysis

Li,

Wen,

Yang

et al. 2024

PLoS ONE

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“…The research on energy-saving optimization of single train mainly focuses on the optimization of timetable and speed profile. For the timetable [6][7][8] optimization of multiple sections of train, it is usually the total running time of given multiple sections, and the time of each interval is reasonably allocated to achieve the goal of energy saving. For speed profile [9,10] optimization, it is to optimize the control strategy of the train to achieve the energy-saving goal, which is the research content of this paper.…”

Section: Related Workmentioning

confidence: 99%

An energy‐efficient train control approach with dynamic efficiency of the traction system

Sun

Zhang

et al. 2023

IET Intelligent Trans Sys

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The efficiency of the train traction system (TTS) changes dynamically with operating conditions. In this paper, a more realistic dynamic efficiency model of TTS is established considering the power flow and non-linear loss of TTS. Based on the real model, the optimal control problem of the train with the minimum electric power consumption on the grid side is constructed. The optimal control condition (OCC) and corresponding control force are derived based on the Pontryagin maximum principle (PMP). A dynamic programming algorithm (DP2) with the OCC curve as the state space is designed to obtain optimal results. The proposed model and the method are validated by the comparison of conventional dynamic programming (DP1) and automatic train operation (ATO) measured data. The simulation results show that compared with the traditional TTS constant efficiency model, considering that the TTS dynamic efficiency model has a better energysaving effect, and the designed DP2 algorithm has a more obvious energy-saving effect than DP1.

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“…Short-term passenger flow prediction (STPFP) is one of the foundations for formulating passenger flow control strategies to alleviate traffic congestion. On the one hand, accurate STPFP results can help the URT department grasp the passenger demand in time and optimize the dispatch of trains to ensure stable operation of the URT systems in response to a surge in passenger flow ( 1 ). On the other hand, it can also provide reliable traffic predictions for passengers and arrange travel reasonably to improve travel efficiency.…”

mentioning

confidence: 99%

Learning Spatial-Temporal Dynamics for Short-Term Passenger Flow Prediction in Urban Rail Transit

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Accurate short-term passenger flow prediction in urban rail transit (URT) plays an important role in ensuring the stable operation of the URT systems. Because of the complex dynamic spatial-temporal dependencies and potential semantic correlations of the URT network, accurate and effective short-term passenger flow prediction is challenging. To solve these problems, a novel model called the dynamic spatial-temporal graph convolutional network (DSTGCN) was proposed. Firstly, spatial semantic graphs (SSGs) were established to encode the spatial dependencies and semantic correlations of the URT network. Meanwhile, the dynamic graph convolutional network (DGCN) with the spatial attention mechanism was used to learn the dynamic spatial correlations of the nodes in the SSGs. Then, the long short-term memory (LSTM) network was integrated into the DGCN to learn the dynamic changes of passenger flow and capture local temporal dependencies. Moreover, the temporal attention mechanism was introduced after LSTM to capture global dynamic temporal correlations by adjusting the weights of different sequence information. Finally, the full connection layers were used to output the prediction results. Several experiments were conducted on Nanning Metro Line 1 real datasets to evaluate the model. The experimental results showed that the DSTGCN can effectively capture the dynamic spatial-temporal dependencies and semantic associations of the passenger flow. Besides, the prediction performances of the DSTGCN were better than those of existing baseline models, and it can provide technical support for improving the intelligent planning and operation decisions of URT systems.

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Improving Synchronization in High-Speed Railway and Air Intermodality: Integrated Train Timetable Rescheduling and Passenger Flow Forecasting

Cited by 18 publications

References 30 publications

Measuring high-speed train delay severity: Static and dynamic analysis

Measuring high-speed train delay severity: Static and dynamic analysis

An energy‐efficient train control approach with dynamic efficiency of the traction system

Learning Spatial-Temporal Dynamics for Short-Term Passenger Flow Prediction in Urban Rail Transit

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