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Recent advancements in high-resolution signal controller data logging and acquisition technologies have enabled development of fundamental performance measures that are effective at identifying split failures and rebalancing green times. However, from a system operator perspective it is important to scale these metrics system-wide for monitoring, triaging, and decision-making uses. This paper articulates the questions that concern a typical system operator, and the associated performance measures and tools that can be used to actively manage such systems macroscopically. To illustrate this methodology, the paper examines 70 signals across seven corridors over a six-month period. Approximately three billion high-resolution signal events including 217 million phase occurrences are analyzed, with 126 million of those phases occurring during the 0600–1900 time period. This paper proposes a scalable method to identify phase occurrences with insufficient green. Applying a previously-developed split failure performance measure for this purpose, over 106,000 split failures (0.08% percent) of the phase occurrences are estimated to have failures for the time of day period. These are displayed using a graphical visualization the authors called the Purdue Split Failure Ticker (PSFT). Subsequent sections of the paper recommend Pareto ranking, drill-down analysis by movement and hour, and corridor movement split summaries to identify where to prioritize resources for signal re-timing activities and geometric improvements.

Scaling Detailed High-Resolution Data Split Performance Measures to Statewide System Level Management