Turning movement spillover (TMS) is the result of a turning bay section (TBS) not being able to accommodate all arriving vehicles, so that the turning-vehicle queue spills back and blocks other vehicles turning in different directions. We are not aware of any TMS estimation method that can remedy this situation or support relevant applications in real time. This research proposes a quasi-real time algorithm for estimating TMS, which includes triggering movement as well as duration estimation. The proposed method is based on data for connected vehicles (CVs), including their trajectories and their desired turning directions. In addition, a model that uses partial trajectory data is proposed. For each assumed TMS, a “simplified trajectory” is developed by the construction of a piece-wise linear curve. To minimize any deviation of the simplified trajectory from observation, a TMS estimation can be made. This proposed method is effective and computationally efficient when tested against dynamic demand in two mainstream signal phase settings, with varied sample sizes. Even though data for a higher number of vehicle samples is generally favorable, the proposed model still makes a good estimate when only one trajectory is available.

转弯运动外溢（TMS）是转弯间隔部分（TBS）无法容纳所有到达的车辆的结果，因此转弯车辆队列向后溢出并阻止其他转向不同方向的车辆。我们不知道有任何TMS估算方法可以纠正这种情况或实时支持相关应用程序。本研究提出了一种估计TMS的准实时算法，该算法包括触发运动以及持续时间估计。所提出的方法基于联网车辆（CV）的数据，包括其轨迹和所需的转弯方向。此外，提出了使用部分轨迹数据的模型。对于每个假定的TMS，通过构建分段线性曲线来开发“简化轨迹”。为了使简化轨迹与观察的任何偏差最小，可以进行TMS估计。当针对具有两个不同样本大小的两个主流信号相位设置中的动态需求进行测试时，该提出的方法既有效又计算高效。尽管一般来说，用于更多车辆样本的数据是有利的，但是当只有一条轨迹可用时，提出的模型仍然可以做出很好的估计。