Detector-Free Signal Offset Optimization with Limited Connected Vehicle Market Penetration: Proof-of-Concept Study

Connected vehicle (CV) data have the potential to transform traffic signal operations, but the success of control methods that are based on CV data depends on the level of market penetration. Recent studies of real-time operational strategies in CV environments suggest that penetrations exceeding 20% is required. This study explored the feasibility of using CV data to generate arrival profiles for optimizing arterial progression. Applications to offline (3-h analysis period) and online (15-min analysis period) offset optimization were considered. Vehicle arrival profiles obtained from real-world measurement were used as a basis for comparison. Subsampled distributions were used to estimate the potential distributions that might be obtained from CVs, and these distributions were statistically analyzed to explore the effects of penetration rate, analysis period, and traffic volume. For selected penetration rates ranging from 0.1% to 50%, the subsampled distributions were used to optimize the corridor, and the results were evaluated in the complete data model. The results show that over a 3-h window, successful offline optimization can be achieved with a CV penetration rate as low as 1%. Layering multiple days of data might allow offline optimization with penetration rates as low as 0.1%. Online optimization with 15-min windows requires somewhat higher penetration rates of at least 5%. The results suggest that early applications of CV data may be possible at very low levels of market penetration. In corridors with a high penetration rate of connected mobile devices, some private-sector probe data services may be on the cusp of providing the necessary data to facilitate detector-free optimization.