基于电子支付数据的公交车厢满载率实时估算方法

针对公交监测和调度中要求实时掌握车厢满载率，以及“一票制”无法获取乘客下车信息等问题，构建基于数据驱动的组合模型，在乘客上车时即推断其出行OD站点，进而融合多源数据实现车厢满载率的实时估算。提出以K近邻算法为组合模型的核心，针对K近邻推断率过低等问题，研究在更大空间维度分析乘客出行规律并推断下车站点的方法，有效提升历史数据的利用率和下车站点的推断率；此外，针对偶发型乘客缺少历史规律数据的情况，充分利用站点下车客流量先验概率随机分配，实现电子支付乘客OD的全样本推断。利用跟车调查法对不同线路、不同班次的车厢拥挤度进行验证。结果表明，模型计算结果与实际结果相符，能够反映出不同线路、不同站段之间的车厢拥挤水平变化。

Real Time Estimation of Bus Loading Rate Based on Bus Electronic Payment Data

Monitoring and scheduling bus operations require real- time grasping of bus loading rate. However, it is normally difficult to obtain the information of passenger drop-off stations in the one-ticket payment system. This study developed a portfolio model to estimate passenger's origin-destination (OD) station after passenger boarding the bus, and then performed the real-time estimation of vehicle loading rate using integrated multi- source data. The portfolio model is based on K-nearest-neighbor algorithm. Considering that there are still portion of passengers whose drop-off station are hard to be estimated, this study used a travel-area-estimation algorithm to obtain passengers' travel rules in a larger spatial dimension. This method effectively improved the estimating rate and utilization rate of historical data. In the occasional case that some historical data of passengers are missing, the study utilized the prior probability of dropoff flows at stations to perform the full sample estimation of passenger's OD in electronic payment system. A carfollowing survey was then used to verify the estimation result of different bus lines and shifts. It shows that the estimated bus loading rate is consistent with the actual results, which reflects the changes of the bus loading level.