Probe vehicle trajectory data has the potential to transform the current practice of traffic signal optimization. Current scalable trajectory data is limited in both the penetration rate and the ping frequency, or the length of time between vehicle waypoints. This paper introduces a methodology to create binary vehicle trajectories which can be used in a neural network to predict when vehicles will arrive at a virtual detector. The methodology allows for vehicles with ping frequencies of up to 60 s to be utilized for the optimization of offsets at signalized intersections. A nine-signal corridor in west Michigan was used to test the proposed methodology. The neural network was compared to traditional linear interpolation strategies and found to improve the root mean squared error of the arrival times by up to 6.18 s. Using the virtual detector data stacked over time to optimize the offsets of the corridor resulted in 77% of the benefit of an offset optimization performed with continuously collected high resolution signal controller data. In the era of big data, this alternative approach can assist with the large-scale implementation of traffic signal performance measures for improved operations.

探测车辆的轨迹数据有可能改变交通信号优化的当前实践。当前的可缩放轨迹数据在穿透率和ping频率或车辆航路点之间的时间长度方面都受到限制。本文介绍了一种创建二进制车辆轨迹的方法，该方法可在神经网络中用于预测车辆何时到达虚拟探测器。该方法可以将ping频率高达60 s的车辆用于信号交叉口的偏移量优化。在密歇根州西部的一个九信号走廊被用来测试所提出的方法。将神经网络与传统的线性插值策略进行了比较，发现将到达时间的均方根误差提高了6.18 s。使用随时间堆叠的虚拟检测器数据来优化通道的偏移量，可以得到使用连续收集的高分辨率信号控制器数据进行偏移量优化的77％的收益。在大数据时代，这种替代方法可以协助大规模实施交通信号性能指标以改善运营。