Improvements to methodologies for signalized intersection capacity analysis and performance measurement

Emtenan, A. M. Tahsin · 2022 · OpenAlex-citations

DOI: 10.31274/td-20240329-413

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Summary

This dissertation addresses critical limitations in current methodologies for analyzing signalized intersection capacity and performance, specifically within the Highway Capacity Manual (HCM) and Automated Traffic Signal Performance Measures (ATSPMs). The research is motivated by the HCM’s lack of robust methods for estimating right-turn-on-red (RTOR) volume and capacity, its inaccurate assumptions regarding throughput under oversaturated conditions, and the sensitivity of ATSPMs to detector configurations. Additionally, it explores whether probe vehicle data can serve as a viable alternative to high-resolution signal controller data for corridor-level performance monitoring. The study comprises four distinct investigations. First, RTOR volume and capacity estimation models were developed using data from 262 signalized intersections across the United States. Regression models for volume were validated against field data, while capacity models utilized gap-acceptance theory and simulation approaches. Second, the impact of turning lane storage length and turning proportions on throughput at oversaturated intersections was analyzed using simulation models and calibrated against real-world scenarios. This work examined how spillback and lane blocking affect saturation flow rates under varying cycle lengths and green durations. Third, the accuracy of ATSPMs was evaluated through simulation modeling, focusing on how detection zone length, lane- versus approach-based detector assignments, and setback distances influence split failure metrics and progression measures. Finally, the correlation between probe vehicle segment speed data and ATSPMs was assessed using field data from intersections in Dubuque, Iowa, comparing high-resolution signal controller logs with probe data over various aggregation intervals. The findings indicate that incorporating RTOR volume and capacity significantly improves the accuracy of level of service estimations, correcting the HCM’s tendency to overestimate delay. The analysis of oversaturated intersections revealed that throughput peaks at moderate cycle lengths and declines with longer cycles due to spillback and blocking, a dynamic not captured by standard HCM saturation flow assumptions. The study demonstrated that detector configuration critically affects ATSPM accuracy; specifically, lane-by-lane detection and optimized detection zone lengths improved the precision of split failure and progression metrics. Furthermore, strong correlations were found between probe vehicle data and ATSPMs, suggesting that probe data is a suitable alternative for monitoring corridor performance when high-resolution signal data is unavailable. These results provide actionable improvements for transportation engineering practice. The developed RTOR models offer a more accurate framework for intersection analysis compatible with existing HCM methodologies. The insights into storage length and turning proportions allow for better signal timing optimization under oversaturated conditions to maximize throughput. The findings on detector configuration guide the proper setup of detection zones to ensure reliable performance measurement. Lastly, the validation of probe data as a proxy for ATSPMs supports cost-effective monitoring strategies for transportation agencies, reducing reliance on expensive high-resolution infrastructure investments.

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