# Median U-Turn Intersection Critical Parameter Research and Operational Performance Evaluation
## 1 Introduction
found that the separation distance is influenced by the number of conflicting driveways, median opening type, and the number of arterial lanes by evaluating median U-turn operations [17]. [PERSON] et al. analyzed how the separation distance impacts the safety performance of vehicles turning around via median U-turns [18]. [PERSON] et al. developed an operational model to determine the optimal location for median U-turns [19]. Secondly, in terms of the gap of median U-turns, related scholars conducted a driving simulation study and found significant differences in driving metrics across various median opening gaps [20]. Finally, in terms of the width of median U-turns, [PERSON] recommended different widths for various types of vehicles to improve the capacity of median U-turns [21]. On the whole, a significant gap in current studies is the integration of these parameters into a comprehensive assessment of overall MUT intersection performance. Most of them have failed to consider variations in traffic volumes, road features, and variable traffic conditions, which may result in inaccuracy and unreliable research findings. In order to better analyze the operational performance of MUT intersections, it is necessary to construct a reasonable model to characterize the actual variations in traffic flow based on diverse traffic volumes and road features. This study fills a significant gap in the existing literature by integrating these critical parameters into a comprehensive assessment model for MUT intersections. Modeling and estimating the traffic movements and vehicle delays at MUT intersections are essential for evaluating intersection performance in complex traffic environments. The results of this research can assist transportation engineers and urban planners in designing safer and more efficient MUT intersections. Selecting appropriate modeling techniques is crucial to accurately reflect the dynamic operations of MUT intersections. Different traffic flow modeling techniques, including cellular automata, microscopic simulation models (e.g., VISSIM), and macroscopic traffic flow models, can be employed to simulate complicated traffic movements and delays at MUT intersections. Several studies have explored different approaches to modeling MUT intersections. [PERSON] et al. developed a model of MUT intersections by using Synchro/SimTraffic (Version 7.0) traffic simulation software to obtain certain performance measures with specific conditions [22]. [PERSON] and his team used VISSIM models for MUT and restricted crossing U-turn (RCUT) intersections to assess the safety and operational aspects of these unconventional designs through a crash analysis, microscopic simulation, and driving simulation [23]. [PERSON] et al. compared the performance of hook-turn (HT) and median U-turn intersections based on VISSIM simulations [24]. [PERSON] et al. proposed a calibrated VISSIM model to estimate the capacity more accurately by incorporating behavioral features of U-turning drivers, such as the priority rule, lane selection, and turning speed [25]. [PERSON] et al. examined optimal distances between median U-turns and signalized intersections by inputting statistical data into the Aimsun (Version 8.3) traffic simulation software [26]. However, many of these studies have overlooked three aspects: (1) Existing vehicle input methods reflecting the randomness and uncertainty of traffic are still not objective. This is because the traffic state of MUT intersections has strong time-varying characteristics. Therefore, the vehicle random input models have to be selected reasonably. (2) There are no detailed regulations and divisions of lane-changing rules for vehicles at intersections. Vehicles must change lanes to the corresponding lane and wait for the traffic light signal before passing the intersections. Then, vehicles with a left-turn demand have to turn around and merge into the opposite lanes. Therefore, this process must be reasonably refined to ensure the accuracy of the simulation.