## 1 Introduction  
The emergence of autonomous systems, such as driverless cars [1, 2] and Fully Automatic Operation (FAO) subway systems [3], has significantly enhanced the operational efficiency of transportation systems while reducing the physical burden on human operators. Automation technology not only improves the reliability of operating systems but also introduces profound changes in the human-machine dynamic. By monitoring and supporting human operations, automation systems can reduce risks. However, in large, complex socio-technical systems, human professionals remain the critical backup, serving as the last line of defense in the event of system failure [4]. This reliance on humans is particularly important because automation inherently places operators in scenarios of uncertainty and incomplete knowledge, making it a pressing challenge to ensure their effectiveness across varying levels of automation [5].  
Despite its benefits, automation does not always simplify or enhance work efficiency. Research on human-automation interaction reveals that it can lead to significant issues,such as the suppression of operators' ability to detect critical signals or alarms [6] and the degradation of operational skills [7]. These problems may leave personnel ill-prepared to protect the system during automation failures. Moreover, increased automation can introduce new failure modes and alter personnel workloads [8], further complicating the human role in automated systems.  
In rail transit systems, for example, dispatchers are primarily responsible for ensuring that trains adhere to their schedules [9]. While advances in signal systems have enabled the automatic correction of minor timing deviations, dispatchers must still maintain a high level of vigilance to prevent catastrophic accidents. A notable case is the head-on collision of two trains in Bad Aibling, Bavaria, in February 2016, which was attributed to a dispatcher's error and an over-reliance on the safety redundancy of the signaling system [10]. This incident underscores the risks of excessive trust in automation and highlights the need for a balanced approach to human-automation interaction.  
The challenges posed by automation extend beyond operational failures. Both excessively high and low workloads can introduce new cognitive and attentional demands [11], necessitating changes in operator training. Furthermore, since automation systems are not infallible, humans remain a crucial component in ensuring system safety. However, humans are not immune to the influence of automation systems. The degree of human reliance on automation in different contexts has thus become a topic of significant interest [12].  
With the rapid advancement of rail transportation technology, Automatic Train Operation (ATO) systems have been widely adopted in many countries [13]. Automatic Train Supervision (ATS), a foundational component of ATO and its next-generation evolution, the FAO system, provides essential monitoring, control, and decision-making capabilities that underpin the functionality and reliability of both ATO and FAO systems. Currently, ATS is primarily used for routine monitoring and emergency response by dispatchers. In the future, within FAO systems, ATS will remain the primary channel through which dispatchers obtain information [14]. The level of trust that dispatchers place in the ATS system will significantly influence the performance of FAO systems.  
Given the critical role of ATS in rail automation, it is essential to investigate the factors that shape dispatchers' trust in ATS and how this trust influences their reliance behaviors. Understanding these dynamics can contribute to refining the theory of automation trust in safety-critical systems and optimizing the performance of FAO systems. Therefore, this study aims to explore the influencing factors of ATS automation trust and its impact on dispatchers' reliance behaviors, providing both theoretical insights and practical implications for the design and operation of future rail automation systems.