Automated, Data-Driven Performance Regime for Operations Management, Planning, and Control

As public transit agencies install new technology systems, these agencies are gaining increasing amounts of data. These data have the potential to change how transit agencies operate by generating better information for decision making. Deriving value from these data and applying it to improve service requires changing the institutional processes that developed when agencies had little reliable information about their systems and customers. This research used the Massachusetts Bay Transportation Authority as a case study. The research assessed how the agency measured performance and then redesigned and advanced the agency's daily performance reports for rapid transit through a collaborative and iterative process with the operations control center staff. These reports were used to identify poor performance, to implement pilot projects to address the causes of poor performance, and to evaluate the effects of these pilots. Through the case study, this research found that service controllers’ trust and interpretation of performance information determined the impact the information had on operations. The results showed that new data would be most effective in producing service improvements if measurements accurately reflected human experience and were developed in conjunction with their intended users. Developing small pilot projects during this collaborative process would also enable new performance information and results in sustainable service improvements.