A Workload Model for Designing & Staffing Future Transportation Network Operations

Across multiple industries (e.g., railroads, airlines, on-demand air taxi services), there are growing investments in future automated transportation systems. Even with these investments, there are still significant human-systems engineering challenges that require deeper investigation and planning. Specifically, fleets that include new levels of automation may require new concepts of how to design and staff network operations centers. Network operations centers have existed for over a century in the railroad and airline industries, where dispatchers have played a central role in safely and efficiently managing networks of railroads and flights. With operators in such safety-critical and time-sensitive positions, workload is the key indicator of their performance in terms of accuracy and efficiency. Yet, there are few tools available for decision-makers in these industries to explore how increasing levels of automation in fleets and operations centers may ultimately affect dispatcher workload.

Thus, this thesis presents a model of dispatcher workload. While automation may be the most pressing change in transportation industries, 10 variables related to configurations of the fleet and the operations center and how those variables interact to influence dispatcher workload were defined. These ten variables come from fleet conditions, strategic design factors, tactical staffing factors, and operational factors. A discrete event simulation was developed to computationally model dispatcher workload with over 10^18 possible configurations of these variables. Additionally, using time-based metrics and integrating results from a prior human reliability assessment, the simulation predicts human error on tasks.

A multi-level validation strategy was developed to build internal, external, and general confidence in using the dispatcher workload model across different domains with data from freight railroad, commuter railroad, and airline operations. In the process of developing and validating the workload model, several other research contributions were made to the field. Eighty-five probability density functions of dispatcher task inter-arrival and service time distributions were generated in the three domains. A data collection tool, Dispatcher’s Rough Assessment of Workload-Over Usual Times (DRAW-OUT), was designed to gather empirical dispatcher-generated estimates of utilization, the proxy for workload, throughout their shifts.

Using the model, experiments were conducted to analyze the sensitivity of dispatcher workload and performance to changes in different parameters. The size of the fleet a dispatcher managed was found to be the most significant factor out of all the other internal parameters. On the other hand, shift schedule, environmental conditions, and operator strategy were the parameters found to have the smallest influence on dispatcher performance. The model was also used to investigate future scenarios that managers could not previously explore due to limitations of time and resources. Results show that the general model is applicable for use in simulating dispatcher workload in both freight and commuter railroad operations as well as airline operations, including short- and long-haul flights, in present-day and future cases.

General confidence was built in the workload model and the Simulator of Humans & Automation in Dispatch Operations (SHADO) was developed as an online platform to provide open access to the underlying discrete event simulation. SHADO is a novel tool that allows stakeholders, including operational managers, to rapidly prototype dispatch operations and investigate human performance in any transportation system. With several theoretical and practical contributions, this work establishes the foundation for future research in the growing field of advanced transportation network operations.