基于阶梯收费刷卡数据的公交下车站点算法优化与实证评估

Optimization and Empirical Evaluation of Passenger Leaving Station in Bus Based on Gradient Charge Swiping Card Data

摘要:

以某市连续5日全天公交阶梯收费刷卡数据、公交出行GPS（global positioning system）数据及公交站点数据为基础，结合出行链算法与随机森林网络，构建了一套公交下车站点融合分析模型。在模型中，首先匹配GPS数据与公交站点数据，确定不同时刻的公交到站信息，再以乘客上车站点位置、出行频率、活动空间、下车点用地类型分布、下车概率为输入，识别乘客下车站点，最终下车站点推算率提升至100%，全样本有效率达76.2%，相比现有基于出行链的方法，识别有效率提升37%。

Abstract:

This paper proposed a fusion analysis model for leaving station identification， based on the 5-day continuous （24 h） bus travel data， bus travel GPS data， and bus stop data of a certain city， in combination with the travel chain method and random forest network. In the model， the GPS data and bus stop data were first matched to determine the bus arrival information at different moments， then the passenger boarding location， travel frequency， activity scope， land use around the leaving station， and the probability of getting off the bus were applied as inputs to identify the passenger leaving station. The final calculation rate is improved to 100%， and the full sample efficiency rate reaches 76.2%. Compared with the existing methods based on the bus travel chain， the recognition efficiency is improved by 37%.