Prediction of rail transit delays with machine learning: How to exploit open data sources

Malek Sarhani , Stefan Voß
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引用次数: 0

Abstract

The use of public transport data has evolved rapidly over the past decades. Indeed, the availability of diverse data sources and advances in analytics have led to a greater emphasis on utilizing data to enhance public transport services. Rail transit systems have increasingly become the preferred mode of travel due to their comfort, speed, and (mostly) emission-free nature. However, persistent delays continue to be a concern. Machine learning-based prediction of transit delays is an emerging field gaining recognition. The first contribution of this paper is to illustrate how to exploit available open data to improve the prediction of rail transit delays using machine learning. Moreover, through a comparison of various well-known machine learning approaches, we show that they can yield significantly different results. Notably, the improved support vector machine method presented in this study exhibits exceptional performance and is well-suited for long-term predictions. Furthermore, we have incorporated explainable artificial intelligence techniques to identify and assess the most significant factors influencing delays. To perform experiments with the method and draw robust conclusions, three case studies featuring different rail services in major cities are provided.

利用机器学习预测轨道交通延误:如何利用开放数据源
过去几十年来,公共交通数据的使用发展迅速。事实上,各种数据源的可用性和分析技术的进步促使人们更加重视利用数据来提升公共交通服务。轨道交通系统因其舒适、快速和(大部分)无排放的特性,日益成为人们首选的出行方式。然而,持续的延误仍然是一个令人担忧的问题。基于机器学习的轨道交通延误预测是一个新兴领域,正逐渐得到认可。本文的第一个贡献是说明如何利用现有的开放数据,通过机器学习改进轨道交通延误预测。此外,通过对各种著名的机器学习方法进行比较,我们发现它们可以产生明显不同的结果。值得注意的是,本研究中提出的改进型支持向量机方法表现出卓越的性能,非常适合长期预测。此外,我们还采用了可解释人工智能技术来识别和评估影响延迟的最重要因素。为了对该方法进行实验并得出可靠的结论,我们提供了三个案例研究,涉及主要城市的不同铁路服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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