Bunching occurs when transit vehicles are unable to maintain their scheduled headways, resulting in two or more vehicles arriving at a stop in close succession and following each other too closely thereafter. Very few studies have explored the prediction of bunching in real-time, particularly for streetcar services. Predicting the time to bunching in real-time allows transit agencies to take more preventive actions to avoid the occurrence of bunching or to minimize its effects. In this study, we present a comprehensive literature review of the recent research conducted in bunching and real-time prediction models. Based on the findings from the literature review, we propose a model for real-time prediction of streetcar bunching. The Kalman filtering model predicts the travel time to bunching incidents and is tested and analyzed using an automatic vehicle location data feed for one streetcar route (Route 506 Carlton), obtained from the Toronto Transit Commission’s next bus system. The results show that: (1) the model provides good predication quality given that it relies only on the real-time GPS feed of streetcars, which makes it practical for use in real-time prediction applications; (2) the model prediction accuracy improves as the transit vehicle travels away from the terminal; and (3) increasing the number of past days involved in the calculations beyond 6 days or increasing the number of leading trips considered in the same day beyond 7 or 10 trips might increase the prediction error.

当过境车辆无法维持其预定的行驶距离时，就会发生拥挤，导致两辆或多辆车辆紧接到达停靠点，此后彼此紧贴。很少有研究探索实时聚集的预测，特别是对于有轨电车服务。实时预测聚束时间可以使运输机构采取更多预防措施，以避免聚束的发生或将其影响最小化。在这项研究中，我们提供了有关在聚类和实时预测模型中进行的最新研究的全面文献综述。基于文献综述的发现，我们提出了一种实时预测路面电车集束的模型。卡尔曼滤波模型可预测到成群事故的发生时间，并使用从多伦多运输委员会的下一个公交系统获得的一条有轨电车路线（506卡尔顿路线）的自动车辆位置数据馈送进行测试和分析。结果表明：（1）该模型仅依赖于有轨电车的实时GPS提要，因此提供了良好的预测质量，这使其在实时预测应用中具有实用性；（2）模型预测精度随着运输车辆离开终点站而提高；（3）将计算中涉及的过去天数增加到6天以上，或者将同一天考虑的前导行程数增加到7或10次以上，可能会增加预测误差。从多伦多运输委员会的下一个公交系统获得。结果表明：（1）该模型仅依赖于有轨电车的实时GPS提要，因此提供了良好的预测质量，使其可用于实时预测应用；（2）模型预测精度随着运输车辆离开终点站而提高；（3）将计算中涉及的过去天数增加到6天以上，或者将同一天考虑的前导行程数增加到7或10次以上，可能会增加预测误差。从多伦多运输委员会的下一个公交系统获得。结果表明：（1）该模型仅依赖于有轨电车的实时GPS提要，因此提供了良好的预测质量，使其可用于实时预测应用；（2）模型预测精度随着运输车辆离开终点站而提高；（3）将计算中涉及的过去天数增加到6天以上，或者将同一天考虑的前导行程数增加到7或10次以上，可能会增加预测误差。（2）模型预测精度随着运输车辆离开终点站而提高；（3）将计算中涉及的过去天数增加到6天以上，或者将同一天考虑的前导行程数增加到7或10次以上，可能会增加预测误差。（2）模型预测精度随着运输车辆离开终点站而提高；（3）将计算中涉及的过去天数增加到6天以上，或者将同一天考虑的前导行程数增加到7或10次以上，可能会增加预测误差。