Predictive models for influence of primary delays using high‐speed train operation records

Li, Zhongcan; Huang, Ping; Wen, Chao; Tang, Yixiong; Xi, Jiang

doi:10.1002/for.2685

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…Then, we attempt to measure the effectiveness of the bias correction approaches in enhancing the prediction accuracy of the prediction models. The % Improvement in RMSE and R 2 is determined through the comparison between DMOLR with correction and the baseline model, that is DMOLR without any correction using eqn (4).…”

Section: Resultsmentioning

confidence: 99%

Prediction of Real-Time Train Arrival Times Along the Swedish Southern Mainline

Tiong

Palmqvist

2022

WIT Transactions on the Built Environment

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Real-time train arrival time prediction is crucial for providing passenger information and timely decision support. The paper develops methods to simultaneously predict train arrival times at downstream stations, including direct multiple output liner regression (DMOLR) and seemingly unrelated regression (SUR) models. To capture correlations of prediction equations, two bias correction terms are tested: (1) one-step prior prediction error and (2) upstream prediction errors. The models are validated on highspeed trains operation data along the Swedish Southern Mainline from 2016 to 2020. The results show that the DMOLR model slightly outperforms the SUR. The DMOLR's prediction performance improves up to 0.32% and 24.03% in term of RMSE and R 2 respectively when upstream prediction errors are considered.

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Section: Resultsmentioning

confidence: 99%

Prediction of Real-Time Train Arrival Times Along the Swedish Southern Mainline

Tiong

Palmqvist

2022

WIT Transactions on the Built Environment

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“…Ref. [ 7 ], on the other hand, employed predictive models based on high-speed train operation records to delve into the influence of primary delays, with a specific concentration on internal factors that contribute to delay severity.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Li,

Wen,

Yang

et al. 2024

PLoS ONE

Self Cite

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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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“…Li et al (2021) found that delays less than three minutes are most vulnerable to the period of delay occurrence. Li et al (2020b) discovered that the average number of affected trains was related to the severity of the delays, which increased with higher average numbers.…”

Section: Explainatory Variablesmentioning

confidence: 99%

Analyzing Factors Contributing to Real-time Train Arrival Delays using Seemingly Unrelated Regression Models

Tiong

Palmqvist

2023

Preprint

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Understanding the impact of various factors on train arrival delays is a prerequisite for effective railway traffic operating control and management. Existing studies analyze the train delay factors using a single, generic regression equation, restricting their capability in accounting for heterogeneous impacts of spatiotemporal factors on arrival delays as the train travels along its route. The paper proposes a set of equations conditional on the train location for analyzing train arrival delay factors at stations. We develop a seemingly unrelated regression equation (SURE) model to estimate the coefficients simultaneously while considering potential correlations between regression residuals caused by shared unobserved variables among equations. The railway data from 2017 to 2020 in Sweden are used to validate the proposed model and explore the effects of various factors on train arrival delays. The results confirm the necessity of developing a set of station-specific train arrival delay models to understand the heterogeneous impact of explanatory variables. The results show that the significant factors impacting train arrival delays are primarily train operations, including dwell times, running times, and operation delays from previous trains and upstream stations. The factors of the calendar, weather, and maintenance are also significant in impacting delays. Importantly, different train operating management strategies should be targeted at different stations since the impacts of these factors could vary depending on where the station is.

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Predictive models for influence of primary delays using high‐speed train operation records

Cited by 12 publications

References 30 publications

Prediction of Real-Time Train Arrival Times Along the Swedish Southern Mainline

Prediction of Real-Time Train Arrival Times Along the Swedish Southern Mainline

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

Analyzing Factors Contributing to Real-time Train Arrival Delays using Seemingly Unrelated Regression Models

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