This paper explores the intricacies of traffic behavior at unsignalized intersections through the lens of a novel dataset, combining manual video data labeling and advanced traffic simulation in SUMO. This research involved recording traffic at various unsignalized intersections in Memphis, TN, during different times of the day. After manually labeling video data to capture specific variables, we reconstructed traffic scenarios in the SUMO simulation environment. The output data from these simulations offered a comprehensive analysis, including time-space diagrams for vehicle movement, travel time frequency distributions, and speed-position plots to identify bottleneck points. This approach enhances our understanding of traffic dynamics, providing crucial insights for effective traffic management and infrastructure improvements.

翻译：本文通过整合人工视频数据标注与SUMO高级交通仿真，基于全新数据集探索无信号交叉口的交通行为复杂性。研究记录了田纳西州孟菲斯市多个无信号交叉口在不同时段的交通流数据。在人工标注视频数据以捕获特定变量后，我们在SUMO仿真环境中重建了交通场景。仿真输出数据提供了综合分析，包括车辆运动的时空图、行程时间频率分布以及用于识别瓶颈点的速度-位置图。该方法加深了我们对交通动力学的理解，为有效的交通管理和基础设施改进提供了关键见解。