Characterize dynamic dilemma zone and minimize its effect at signalized intersections.
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Characterize dynamic dilemma zone and minimize its effect at signalized intersections.

  • 2008-12-26

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  • English

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      Dilemma zone at signalized intersection has been recognized as a major potential causing rearend and right-angle crashes, and has been widely studied by researches since it was initially proposed as the GHM model in 1960. However, concepts conventionally defined to represent the yellow phase dilemma lack integrity. This research conducts a comprehensive literature review with attempt to clarify the interrelationship among dilemma zone, option zone, and indecision (decision) zone, and to develop a heuristic framework to present the contributing factors in dilemma zone modeling. A new method for modelling the locations and lengths of the dilemma zone using video-capture techniques and vehicle trajectory data is presented in this report. First, dilemma zone is mathematically modeled based on the GHM model. Then, field-observed trajectory data extracted by the video-capturing-based approach are used to calibrate the contributing factors involved in the dilemma zone model. The high accuracy of the time-based trajectory data has significantly enhanced the accuracy of the calibrated dilemma zone models. Two sets of trajectories are explored for calibrating the dilemma zone contributing factors. One is concerned with maximum yellow-onset safe passing distance and minimum yellow-onset stopping distance. The other is concerned with Xth percentile yellow-onset passing distance and (100-X)th percentile stopping distance for the prevailing travel behaviors. The latter alternative actually precludes “too conservative” and “too aggressive” maneuvers in response to yellow indications. One critically important result is the dilemma zone look-up charts that are developed based on the calibrated dilemma zone models. Such charts provide a convenient tool to identify the locations and lengths of dilemma zones for any speed and yellow duration conditions. Additionally, impact of arrival type and vehicle types are also explored. Results reveal that traffic in a good progression (Arrival Type ≥ 4) has a further option zone. It is also discovered that the length of option zone decreases as the vehicle size increases, while the downstream boundary of option zone is further from the stop line as the vehicle size increases. In overall, this project aims to conduct a preliminary research for providing a proof of concept about the modeling of dynamic dilemma zones, and validating the feasibility of the methodology for calibrating the dilemma zone model using trajectory data. The methodology used in this study establishes a solid basis for future research of the optimum signal detection placement and related dilemma zone protection problems with consideration of multi-speed protection. This project is granted by the Ohio Transportation Consortium (OTC). The research team is thankful to Dr. Ping Yi at University of Akron and staffs at OTC for their strong support to the project. Gratitude goes to Ph.D. students, Mr. Zhixia Li and Mr. Qingyi Ai, and M.S. students, Mr. Vijay Krishna Nemalapuri and Mr. Sudhir Reddy Itekyala for their effective assistances in field data collection. In particular, Mr. Zhixia Li took the lead in data collection and analysis and participated in drafting the report. Finally, the research team also expresses our thanks to Ms. Brenda Slaughter, senior Grant Administrator at UC Sponsored Research Services and Mr. Tom Davis, senior Grant Administrator at UC Department of Civil and Environmental Engineering for their administrative support. This research could be not successfully finished without all their active participations, critical contributions, and strong supports.
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