A novel hybrid approach based on CREAM and fuzzy ANP to evaluate human resource reliability in the urban railway

Abstract

Nowadays, various factors such as the welfare of citizens as well as traffic and air pollution reduction demonstrate the necessity of the creation and development of urban rail transport systems. Despite the utilization of new technologies in transportation systems and their automation, humans continue to play an important role in different parts of design, operation, monitoring, and repair. All activities performed in these sectors are affected by special working conditions or workplaces. Therefore, in order to decrease human error and improve safety, it is essential to identify and evaluate operating conditions affecting the condition performance commons (CPCs) and determine the general action failure probability using a suitable method. One way to do so is by using the cognitive reliability and error analysis method (CREAM). So far, in studies based on CREAM, the effects of these factors have been assumed to be equal when evaluating the effects of CPCs. However, in real terms, the effect of these factors can vary regarding the workplace and employees. In the present study, the fuzzy analytic network process (FANP) has been employed to determine the weight of efficient parameters in order to investigate and evaluate the working conditions affecting employee performance as a theoretical contribution. Urban rail transport systems such as the subway, tram, and urban railway have multiple entities, all contributing to a safe and efficient transportation system. These systems are composed of multiple human actors (driver, traffic control, and communication and licensing operator) and signaling systems working in a synchronized procedure. Because of the importance of human resources in these systems, the application of this method has been shown in the Mashhad urban railway in Iran as a practical contribution. According to the results of the basic CREAM, six factors of CPCs reduce performance reliability, most notably the adequacy of the organization; adequacy of the man-machine-interface (MMI) and operational support;” and “adequacy of training and preparation,” which have more effects on performance reliability compared to other factors. Based on the results obtained from the extended CREAM, the highest amount of detected error belonged to execution failure (%85), observation failure (%11), and planning failure (%4).