Tzu-Hao Yan


Last Name

Yan

First Name

Tzu-Hao

ORCID

0000-0002-0359-1569

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2019 - 201912020 - 202510
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Publications 1 - 10 of 11
  • How well do train drivers estimate track conditions?
    Item type: Other Conference Item
    Yan, Tzu-Hao; Corman, Francesco (2021)
  • The development of a status prediction model for railway tracks from on-board monitoring data
    Item type: Other Conference Item
    Yan, Tzu-Hao; Corman, Francesco (2022)
    Monitoring the tracks' status by inspection trains is a common procedure for maintaining the railway system safety. However, railway regulators nowadays are facing the challenge of lacking time and resources to perform inspections due to increasing traffic demand. One possible solution for this is applying the On-Board Monitoring (OBM) technique, which aims to use commercial trains for monitoring the tracks’ status. This allows railway regulators to perform more inspections without affecting the traffic and using expensive inspection trains. However, the new OBM data differ from those collected by common inspection trains in several aspects, including lower data quality, higher monitoring frequencies and fewer features recorded. Therefore, new methods should be developed for applying the new data. This study develops a Markov model for predicting the tracks' status using OBM data. Practical regulations and thresholds are applied for setting the states in the model. Real data collected from the Switzerland railway network are used for verifying the model. Results show that the proposed model is capable of predicting the degradation of the tracks' status and the occurrence of the Drivers Response Failures (DRF), which can assist the railway regulators in scheduling maintenance tasks.
  • Assessing and extending track quality index for novel measurement techniques in railway
    Item type: Other Conference Item
    Yan, Tzu-Hao; Corman, Francesco (2020)
    2020 TRB Annual Meeting Online
    A systematic maintenance process is essential to keeping the railway system safe and reliable. However, performing such maintenance is costly and often results in system disruption. There is a tradeoff between system safety and budgetary constraints; understanding the condition of the track infrastructure in essential to find the balance between needs and costs for decisions about when to perform maintenance. In this study, the Track Quality Index (TQI), which is commonly used to evaluate the status of the tracks and to deciding maintenance interventions, is reviewed, including twelve TQIs for superstructure and six TQIs for substructure. The results from the literature review indicate that TQIs for sleeper and subgrade have not been developed. The differences among TQIs are compared and discussed using a set of hypothetical raw data. Their capabilities for identifying track irregularities are also investigated based on the EN 13848 regulations. Four concepts, including accuracy, sensitivity, data required and parameters considered are proposed to classify TQIs’ characteristics. The results suggest that there exists tradeoff between the four concepts, where high sensitivity can increase the ability to detect the smallest defects but also easy getting affect by bias; more parameters considered may indicate low accuracy when detecting single type of defect. Therefore, this study suggests railway regulators to use multiple TQIs with complementary characteristics for classifying the track status.
  • Developing and extending status prediction models for railway tracks based on on-board monitoring data
    Item type: Other Conference Item
    Yan, Tzu-Hao; De Almeida Costa, Mariana; Corman, Francesco (2023)
    2023 TRB Annual Meeting Online Program Archive
    Assigning inspection trains to monitor the tracks' quality is a standard procedure for maintaining railway systems' safety. The main challenges lie in lacking time and resources to perform the inspections due to the increasing traffic nowadays. To overcome these challenges, many consider adopting the On-Board Monitoring (OBM) technique for performing the inspections. This technique assigns commercial trains, instead of traditional Track Recording Vehicles (TRV), to monitor the tracks' status, allowing railway operators to perform more inspections without affecting the traffic and using expensive inspection trains as well. However, compared with TRV data, the new OBM data are of lower data quality and fewer features, although they can be recorded more frequently. Therefore, new methods should be developed for effectively applying the new data. This study develops four models, including the linear regression (LR) model, Markov model, Ordinary Kriging model and Kalman filter (KF) model, for predicting the tracks' status based on the OBM data. Data collected from the Switzerland railway network are used for verifying the models. Results show that the proposed models can effectively predict the degradation of tracks' status in different ways and, therefore, assist the railway regulators in scheduling maintenance tasks.
  • Assessing and extending track quality index for novel measurement techniques in railway systems
    Item type: Journal Article
    Corman, Francesco; Yan, Tzu-Hao (2020)
    Transportation Research Record
    A systematic maintenance process is essential to keeping railway systems safe and reliable. However, performing such maintenance is costly and often results in system disruption. There is a tradeoff between system safety and budgetary constraints; understanding the condition of the track infrastructure is essential to find the balance between needs and costs for decisions about when to perform maintenance. In this study, the track quality index (TQI), which is commonly used to evaluate the status of tracks and to decide maintenance interventions, is reviewed, including 12 TQIs for superstructure and six for substructure. A literature review indicates that TQIs for sleepers and subgrade have not yet been developed. The differences between TQIs are compared using a set of hypothetical raw data. Their capabilities for identifying track irregularities are also investigated based on the EN 13848 regulations. To classify TQI characteristics in a systematic way, this study proposes four concepts: accuracy, sensitivity, data required, and specificity. Accuracy indicates a TQI’s capability of detecting defects; sensitivity indicates how TQIs change according to variations in the defects; specificity relates to the amount of parameters considered, and the ability to pinpoint root causes or global consequences of defects. The results suggest a tradeoff between the four concepts, where high sensitivity can increase the ability to detect the smallest defects but may be affected by bias; more parameters considered may indicate low accuracy when detecting a single type of defect. Therefore, this study suggests railway regulators use multiple TQIs with complementary characteristics for classifying track status.
  • OMISM - On board monitoring for integrated systems understanding & management improvement in railways
    Item type: Other Conference Item
    Yan, Tzu-Hao; Hoelzl, Cyprien (2019)
  • Scheduling tamping actions with consideration of drivers response failures
    Item type: Other Conference Item
    Yan, Tzu-Hao; Corman, Francesco (2023)
  • Integration of on-board monitoring data into infrastructure management for effective decision-making in railway maintenance
    Item type: Journal Article
    Yan, Tzu-Hao; Hoelzl, Cyprien; Corman, Francesco; et al. (2025)
    Railway Engineering Science
    Railway infrastructure is a crucial asset for the mobility of people and goods. The increased traffic frequency imposes higher loads and speeds, leading to accelerated infrastructure degradation. Asset managers require timely information regarding the current (diagnosis) and future (prognosis) condition of their assets to make informed decisions on maintenance and renewal actions. In recent years, in-service vehicles equipped with on-board monitoring (OBM) measuring devices, such as accelerometers, have been introduced on railroad networks, traversing the network almost daily. This article explores the application of state-of-the-art OBM-based track quality indicators for railway infrastructure condition assessment and prediction, primarily under the prism of track geometry quality. The results highlight the similarities and advantages of applying track quality indicators generated from OBM measurements (high frequency and relatively lower accuracy data) compared to those generated from higher precision, yet temporally sparser, data collected by traditional track recording vehicles (TRVs) for infrastructure management purposes. The findings demonstrate the performance of the two approaches, further revealing the value of OBM information for monitoring the track status degradation process. This work makes a case for the advantageous use of OBM data for railway infrastructure management, and attempts to aid understanding in the application of OBM techniques for engineers and operators.
  • On board monitoring for integrated systems understanding & management improvement in railways (OMISM)
    Item type: Conference Poster
    Yan, Tzu-Hao; Hoelzl, Cyprien; Dertimanis, Vasilis; et al. (2022)
Publications 1 - 10 of 11