The paper proposes a methodology for providing personalized, predictive in-vehicle crowding information to public transport travellers via mobile applications or at-stop displays. Three crowding metrics are considered: (1) the probability of getting a seat on boarding, (2) the expected travel time standing, and (3) the excess perceived travel time compared to uncrowded conditions. The methodology combines prediction models of passenger loads and alighting counts based on lasso regularized regression and multivariate PLS regression, a probabilistic seat allocation model and a bias correction step in order to predict the crowding metrics. Depending on data availability, the prediction method can use a combination of historical passenger counts, real-time vehicle locations and real-time passenger counts. We evaluate the prediction methodology in a real-world case study for a bus line in Stockholm, Sweden. The results indicate that personalized, predictive crowding information that is robust to varying data availability can be provided sufficiently early to be useful to travellers. The methodology is of value for agencies and operators in order to increase the attractiveness and capacity utilization of public transport.

本文提出了一种通过移动应用程序或车站显示屏向公共交通旅客提供个性化，可预测的车载拥挤信息的方法。考虑了三个拥挤指标：（1）登机的机会；（2）预计的站立时间；（3）与未拥挤情况相比，多余的感知旅行时间。该方法结合了基于套索正则化回归和多元PLS回归的乘客负载和下车计数的预测模型，概率座位分配模型和偏差校正步骤，以预测拥挤指标。根据数据的可用性，预测方法可以使用历史乘客数量，实时车辆位置和实时乘客数量的组合。我们在瑞典斯德哥尔摩的公交线路的实际案例研究中评估了预测方法。结果表明，对于变化的数据可用性具有鲁棒性的个性化，预测性拥挤信息可以足够早地提供给旅行者。该方法对于代理商和运营商具有重要意义，以提高公共交通的吸引力和容量利用率。