Research on Quantitative Evaluation of Emergency Handling Workload of Railway Dispatcher

In the railway transportation system, train dispatchers are the center of the transportation organization and command system, shouldering the core work of railway transportation management. In emergency response, dispatchers perform multiple tasks in a short time. In this case, dispatchers are in a state of high tension, and their workload surges. Due to time pressure or lack of familiarity and standardization with emergency response operations, dispatchers are likely to miss or misoperate. Thus, the authors propose a novel method to evaluate the workload of emergency response operations from a quantitative and structural perspective, which is helpful to reduce human safety risks for train dispatchers. The proposed method is based on the dispatcher's time multiple resource theory, the emergency work of dispatchers' tasks are analyzed and classified in progressive structure, using a sequence of 28 behavioral elements from four information processing sources: visual, auditory, cognitive, and motor response. The subtasks are scored based on the VACP scale, and the total load is corrected by considering the interference factor weights that exist between different operational tasks in the channels. Based on single task analysis and multi-resource theory, the workload of railway dispatchers can be established. Considering the load caused by resource competition conflicts to calculate the total workload. Taking the disposal of overhead contact system suspended foreign matters as an example, the effectiveness of the method was verified by combining the emergency disposal procedures for overhead contact system suspended foreign matters in a railway bureau. The results show that this method can describe the changes in workload during the entire operation process, accurately identify the stages and operations with high workload, and has great practical significance for improving the operational reliability of railway train dispatchers, reducing operational risks, and reducing errors.