Infrastructure-Free Optimization of Traffic Signal Offsets: Identifying Traffic Flow Patterns and Reverse Engineering Signal Timing with Connected Vehicle Data

Abstract

Connected vehicle (CV) data holds scalable and transformative potential for traffic management and signal offset optimization in a completely infrastructure-free environment. This study explores this potential by leveraging CV data to reverse engineer basic signal timing parameters and propose an innovative visualization tool, the “offset adjustment diagram.” This tool estimates and displays current and predicted traffic conditions for different offset adjustments, incorporates trial offset adjustments along a corridor, and uses trajectory data from real-world and simulated environments to evaluate improvement opportunities. An example from a corridor in Ankeny, Iowa, illustrates corridor-level screening of traffic flow using the proposed corridor screening tool. A simulation model was created for before/after scenario analysis, with a comparison between offset adjustment diagrams from different data sources. In addition, the application of the tool for improving signal timing with real-world CV data along the Ankeny corridor is demonstrated. The analysis reveals that trajectory-based data may generate less comprehensive information than signal event-based data; however, the outcomes have significant potential. Trajectory-derived offsets can considerably improve travel time, serving as a valuable surrogate for event-based offset optimization. This approach is valuable in areas lacking vehicle detection infrastructure, providing substantial benefits in traffic operations and overall corridor performance optimization.