Queue dissipation has been extensively studied about traffic signalization, work zone operations, and ramp metering. Various methods for estimating the intersection’s queue length and dissipation time have been reported in the literature, including the use of car-following models with simulation, vehicle trajectories from GPS, shock-wave theory, statistical estimation from traffic flow patterns, and artificial neural networks (ANN). However, most of such methods cannot account for the impacts of interactions between different vehicle types and their spatial distributions in the queue length on the initial discharge time and the resulting total dissipation duration. As such, this study presents a system, named TrafficTalk, that applies a deep learning-based method to reliably capture the queue characteristics of mixed traffic flows, and produce a robust estimate of the dissipating duration for the design of the optimal signal plan. The proposed TrafficTalk, featuring the effectiveness in transforming video-imaged traffic conditions into vehicle density maps, has proved its performance under extensive field evaluations. For instance, compared with the benchmark model, XGBoost in the literature, it has reduced the MAPE from 25.8% to 10.4%., and from 31.3% to 10.4% if the queue discharging stream comprises motorcycles.

中文翻译：

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利用深度学习预测混合交通流的队列消散时间


队列消散在交通信号、工作区操作和坡道计量方面得到了广泛的研究。文献中报道了估计交叉路口排队长度和消散时间的各种方法，包括使用仿真跟车模型、GPS 车辆轨迹、冲击波理论、交通流模式统计估计以及人工神经网络（安）。然而，大多数此类方法无法解释不同车辆类型之间的相互作用及其在队列长度中的空间分布对初始放电时间和最终的总消散持续时间的影响。因此，本研究提出了一个名为 TrafficTalk 的系统，该系统应用基于深度学习的方法来可靠地捕获混合交通流的队列特征，并为设计最佳信号计划提供耗散持续时间的稳健估计。所提出的 TrafficTalk 具有将视频图像交通状况转化为车辆密度图的有效性，并已在广泛的现场评估中证明了其性能。例如，与文献中的基准模型XGBoost相比，它已将MAPE从25.8%降低到10.4%，如果队列卸货流中包含摩托车，则将MAPE从31.3%降低到10.4%。