Trends, Topics, Leaders, Influential Studies, and Future Challenges of Machine Learning Studies in the Rail Industry

Yong, Gunwoo; Lee, Ghang

doi:10.1061/(asce)is.1943-555x.0000691

Cited by 2 publications

(1 citation statement)

References 68 publications

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“…Passenger flow prediction methods have been studied in the past decades [9,10] and applied extensively, such as in the bus transportation system [11], in the metro system [12], in the railway system [13], and in the airline system [14], etc. Various types of models have been proposed to achieve a high prediction accuracy of passenger flow in recent years [15,16].…”

Section: Related Literature Reviewmentioning

confidence: 99%

Short-Term Prediction of Time-Varying Passenger Flow for Intercity High-Speed Railways: A Neural Network Model Based on Multi-Source Data

Peng

2023

Mathematics

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The accurate prediction of passenger flow is crucial in improving the quality of the service of intercity high-speed railways. At present, there are a few studies on such predictions for railway origin–destination (O-D) pairs, and usually only a single factor is considered, yielding a low prediction accuracy. In this paper, we propose a neural network model based on multi-source data (NN-MSD) to predict the O-D passenger flow of intercity high-speed railways at different times in one day in the short term, considering the factors of time, space, and weather. Firstly, the factors that influence time-varying passenger flow are analyzed based on multi-source data. The cyclical characteristics, spatial and temporal fusion characteristics, and weather characteristics are extracted. Secondly, a neural network model including three modules is designed based on the characteristics. A fully connected network (FCN) model is used in the first module to process the classification data. A bi-directional Long Short-Term Memory (Bi-LSTM) model is used in the second module to process the time series data. The results of the first module and the second module are spliced and fused in the third module using an FCN model. Finally, an experimental analysis is performed for the Guangzhou–Zhuhai intercity high-speed railway in China, in which three groups of comparison experiments are designed. The results show that the proposed NN-MSD model can predict many O-D pairs with a high and stable accuracy, which outperforms the baseline models, and multi-source data are very helpful in improving the prediction accuracy.

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Section: Related Literature Reviewmentioning

confidence: 99%

Short-Term Prediction of Time-Varying Passenger Flow for Intercity High-Speed Railways: A Neural Network Model Based on Multi-Source Data

Peng

2023

Mathematics

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A data-driven prioritisation framework to mitigate maintenance impact on passengers during metro line operation

Consilvio,

Vignola,

López Arévalo

et al. 2024

Eur. Transp. Res. Rev.

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The application of artificial intelligence (AI) techniques may lead to significant improvements in different aspects of rail sector. Considering asset management and maintenance, AI can improve data analysis and asset status forecasting and decision-making processes, fostering predictive and prescriptive maintenance strategies. A prescriptive approach should be able to predict future scenarios as well as to suggest a course of actions. Nevertheless, the decision-making in rail asset management is often based on the classical asset-oriented approach, concentrating on the function of the asset itself as a main key performance indicator (KPI), whereas a user-oriented approach could lead to improved performance in terms of level of service. This paper is aimed at integrating the passengers’ perspective in the decision-making process for asset management to mitigate the impact that service interruptions may have on the final users. A data-driven prioritisation framework is developed to prioritise maintenance interventions taking into account asset status and criticality. In particular, a three-step approach is proposed, which focuses on the analysis of passenger data to evaluate the failure impact on the service, the analysis of alarms and anomalies to evaluate the asset status, and the suggestion of maintenance interventions. The proposed approach is applied to the maintenance of the metro line M5 in the Italian city of Milan. Results show the usefulness of the proposed approach to support infrastructure managers and maintenance operators in making decisions regarding the priority of maintenance activities, reducing the risk of critical failures and service interruptions.

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Trends, Topics, Leaders, Influential Studies, and Future Challenges of Machine Learning Studies in the Rail Industry

Cited by 2 publications

References 68 publications

Short-Term Prediction of Time-Varying Passenger Flow for Intercity High-Speed Railways: A Neural Network Model Based on Multi-Source Data

Short-Term Prediction of Time-Varying Passenger Flow for Intercity High-Speed Railways: A Neural Network Model Based on Multi-Source Data

A data-driven prioritisation framework to mitigate maintenance impact on passengers during metro line operation

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