Understanding how vehicle drivers and pedestrians interact is key to identifying countermeasures that improve the safety of the interactions. As a result, techniques that can be used to evaluate the effectiveness of safety countermeasures and traffic control devices without the need to wait for the availability of crash data are needed. Using video, the interactions between right-turning vehicles and conflicting pedestrians were documented and quantified using vehicle and pedestrian position timestamps. Interactions documented were purposely narrow in scope to obtain a controlled dataset. Logged timestamps enabled the calculation of values such as time to complete a right turn and time for a pedestrian to reach a critical conflict point when a vehicle initiated a right turn. A nonprobabilistic regression model explaining the relationship between the calculated values was created. The model described the expected behavior of right-turning drivers: when drivers perceive the possibility of a pedestrian reaching a critical conflict point at the same time as them, they will modify their behavior, even if not coming to a complete stop. This behavior is not a surprise and has been previously documented in the literature. The primary contribution of this research is demonstrating that by analyzing a narrow set of interactions, clear and simple models that mostly explain the interactions between right-turning vehicles and pedestrians can be developed using nonprobabilistic linear regression techniques. An argument is made that the model parameters can be used to evaluate the effectiveness of traffic control devices.

了解车辆驾驶员和行人之间的互动方式是确定提高互动安全性的对策的关键。结果，需要可用于评估安全对策和交通控制设备的有效性而无需等待碰撞数据的可用性的技术。通过视频，右转车辆和冲突行人之间的交互被记录下来，并使用车辆和行人位置时间戳进行量化。记录下来的交互作用的范围故意狭窄，以获得受控的数据集。记录的时间戳可以计算值，例如完成右转弯的时间以及当车辆启动右转弯时行人到达关键冲突点的时间。创建了一个非概率回归模型，该模型解释了计算值之间的关系。该模型描述了右转驾驶员的预期行为：当驾驶员察觉到行人与他们同时到达关键冲突点的可能性时，即使他们没有完全停止，他们也会改变其行为。这种行为不足为奇，并且先前已在文献中进行了记录。该研究的主要贡献在于，通过分析狭窄的相互作用集，可以使用非概率线性回归技术来开发清晰，简单的模型，这些模型主要用于解释右转车辆与行人之间的相互作用。有人认为模型参数可用于评估交通控制设备的有效性。