Diverging Diamond Interchange Performance Measures Using Connected Vehicle Data
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Summary
This study addresses the lack of empirical performance analysis for Diverging Diamond Interchanges (DDIs) using connected vehicle (CV) data. While DDIs have been widely implemented to improve safety and reduce construction costs, existing performance evaluations rely heavily on simulation models, which require extensive calibration, or historical crash data, which necessitates years of collection. The authors argue that CV trajectory data offers a scalable, infrastructure-light alternative to assess operational metrics such as delay, progression, and saturation without the limitations of point sensors or simulation biases. The research focuses on a DDI located at I-69 and E Dupont Road in Fort Wayne, Indiana, which opened in 2014. The authors analyzed private-sector CV trajectory data collected between June 7 and 11, 2021, from 5:00 AM to 10:00 PM. The dataset comprised over 7,000 vehicle trajectories and 130,000 GPS points, with an estimated penetration rate of 4.6%. Each data point included speed, heading, location, and timestamp with a 3-second reporting interval and 1.5-meter positional accuracy. To process this data, the authors developed an extension of the Purdue Probe Diagram (PPD), termed the DDI PPD, which linearly references vehicle progression relative to the downstream intersection and color-codes segments by the number of stops. They also introduced a variation of this diagram to visualize queue dynamics within the critical interior crossover storage. The findings demonstrate that the proposed methodologies effectively quantify key performance measures, including arrivals on green (AOG), level of service (LOS), split failures, and downstream blockage. Analysis of the DDI PPD revealed that eastbound traffic from the external street and northbound traffic from the ramp experienced the most significant delays, approaching intersections farthest from free-flow trajectories. Conversely, westbound external and northbound ramp traffic exhibited the highest AOG rates, indicating superior progression. The study identified specific instances of split failures, where vehicles required two or more stops at a signal, indicating oversaturation. For example, eastbound external traffic experienced split failures at both crossover signals, while northbound ramp traffic faced split failures at the second signal. The progression DDI PPD further highlighted unbalanced flow dynamics, showing that while most eastbound through traffic stopped at the second signal, northbound left-turning ramp traffic cleared the crossover storage efficiently. The significance of this work lies in providing transportation agencies with a robust, near-real-time tool for evaluating DDI performance. By leveraging commercially available CV data, agencies can identify specific movements and time-of-day periods requiring signal timing adjustments without investing in new sensing infrastructure. The authors conclude that these techniques are scalable and can be applied to any DDI globally, facilitating the identification of best practices for operating these complex interchanges and ensuring that crossover storage remains uncongested to prevent spillback onto interstate ramps.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-25 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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