Abstract
In this study, a hybrid method combining extreme learning machine (ELM) and particle swarm optimization (PSO) is proposed to forecast train arrival delays that can be used for later delay management and timetable optimization. First, nine characteristics (e.g., buffer time, the train number, and station code) associated with train arrival delays are chosen and analyzed using extra trees classifier. Next, an ELM with one hidden layer is developed to predict train arrival delays by considering these characteristics mentioned before as input features. Furthermore, the PSO algorithm is chosen to optimize the hyperparameter of the ELM compared to Bayesian optimization and genetic algorithm solving the arduousness problem of manual regulating. Finally, a case is studied to confirm the advantage of the proposed model. Contrasted to four baseline models (k-nearest neighbor, categorical boosting, Lasso, and gradient boosting decision tree) across different metrics, the proposed model is demonstrated to be proficient and achieve the highest prediction accuracy. In addition, through a detailed analysis of the prediction error, it is found that our model possesses good robustness and correctness.
Highlights
With the rapid development of society and the continuous improvement of people’s quality of life, people have put forward higher requirements for the reliability and punctuality of high-speed railway transportation [1]
E traditional models are a classical approach for train delay prediction, such as probability distribution models [4, 5], regression models, event-driven methods, and graph theorybased approaches
For the probability distribution model, Higgins and Kozan proposed an exponential distribution model, which applied a three-way, two-block station train delay propagation signal system, to estimate delays of trains caused by train operational accidents [4]. rough the assessment of the linear relationship between several independent features and dependent features [2], regression models were widely employed to predict train delays, dwell times, and running times [6, 7]
Summary
With the rapid development of society and the continuous improvement of people’s quality of life, people have put forward higher requirements for the reliability and punctuality of high-speed railway transportation [1]. [2] proposed a support vector regression model in train delay problem of passenger train, which captured the relationship between the arrival delay and a variety of changing external factors, and compared it with the artificial neural networks. Erefore, a new study that combined a shallow ELM and a deep ELM tuned via the threshold out technique was employed to predict train delays, taking the weather data into account [23] Parameter adjustment is another critical factor to guarantee the good performance of machine learning models [24, 25].
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