Urban rail transit systems (URTSs) have increasingly become the backbone of modern public transportation, attributed to their unparalleled convenience, high efficiency, and commitment to sustainable green energy. The authors 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 generates considerable data, offering substantial opportunities for service quality enhancements. machine learning (ML), with its demonstrated proficiency in extracting valuable insights from vast data, holds significant promise in the quest to empower URTS. This survey presents a comprehensive exploration of the potential application of ML in URTS. Initially, they delve into the existing challenges of URTS, thereby elucidating the compelling motivation behind the integration of ML into these systems. They then propose a taxonomy of ML paradigms and techniques, discussing indepth their potential applications in URTS, encompassing perception, prediction, and optimization tasks. Subsequently, they 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, they 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.
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IEEE Transactions on Intelligent Transportation Systems ; 25 , 3 ; 2174-2181
2024-03-01
106493 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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