Machine Learning in Urban Rail Transit Systems: A Survey

Urban Rail Transit Systems (URTS) have increasingly become the backbone of modern public transportation, attributed to their unparalleled convenience, high efficiency, and commitment to sustainable green energy. As we witness a global resurgence of urban rail transit, it becomes evident that most existing URTS still operate on a level of suboptimal intelligence, with their operation and maintenance methods lagging behind other advanced urban transit systems. URTS generate considerable data, offering substantial opportunities for service quality enhancements. Machine Learning (ML), with its demonstrated proficiency in extracting valuable insights from vast data, hold significant promise in the quest to empower URTS. This survey presents a comprehensive exploration of the potential application of ML in URTS. Initially, we delve into the existing challenges of URTS, thereby elucidating the compelling motivation behind the integration of ML into these systems. We then propose a taxonomy of ML paradigms and techniques, discussing in-depth their potential applications in URTS, encompassing perception, prediction, and optimization tasks. Subsequently, we scrutinize a plethora of ML-empowered URTS application scenarios, including but not limited to obstacle perception, infrastructure perception, communication and cybersecurity perception, passenger flow prediction, train delay prediction, fault prediction, remaining useful life (RUL) prediction, train operation and control optimization, train dispatch optimization, and train ground communication optimization. Finally, we present an insightful discussion on the challenges and future directions for URTS, aiming to harness the full potential of ML techniques to deliver superior service and performance.

I. Introduction

Researchers have become increasingly interested in Machine Learning (ML) approaches in recent years. ML deals with how to develop computer models based on experience and automatically improve them [1]. It is one of the most quickly developing technological fields at the intersection of probability, statistics, computer science, and data science. The empirical cost of ML continues to fall thanks to the rapid development of big data analytics. Furthermore, as underlying hardware has improved, computing multidimensional data arithmetic and time costs have decreased, making data-intensive ML practical. As a result of those developments mentioned above, new theories and ML methods are constantly being introduced. When combined with emerging technologies like edge computing, next-generation communication technologies, and cloud computing, ML plays an indispensable role in healthcare, smart home, smart manufacturing, education, finance and transportation.