Bus passenger flow classification prediction driven by CNN-GRU model and multi-source data
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摘要: 为精准分析公交线路与站点不同客流的出行特征及时变差异性，结合深度学习理论，提出了一种基于卷积神经网络(CNN)与门控制循环单元(GRU)组合的公交客流分类预测模型；融合匹配公交一卡通刷卡、公交车GPS轨迹、线路和站点基础信息、气象等多源数据，实现公交客流数据重构；采用K-Medians算法将乘客分为通勤类和非通勤类；以乘客类型、历史客流量、时段、高/平峰、星期、降水量、重大活动等因素为输入向量，分别建立CNN与GRU单一模型，并利用均方误差、均方根误差、平均绝对误差为评价指标，开展预测；针对单一模型不适用多特征时间序列预测等问题，分别构建了由CNN和GRU组合的线路客流和断面客流预测模型；以北京市特15路公交为例，预测工作日与非工作日场景下的线路及断面的分类客流。分析结果表明：对于通勤类和非通勤类线路及断面客流，组合模型的均方误差相比单一模型平均降低了57.932、13.106和33.987，均方根误差平均降低了1.862、1.058和1.538，平均绝对误差平均降低了1.399、0.487和0.613，可见，多源数据驱动下的CNN-GRU组合模型具有良好的预测性能。Abstract: To accurately analyze the trip characteristics and time-varying differences of different passenger flows of bus routes and stops, combined with deep learning theory, a bus passenger flow classification prediction model based on a combination of a convolutional neural network (CNN) and gated recurrent unit (GRU) was proposed. By integrating and matching multi-source data, such as bus card swiping, bus global positioning system (GPS) trajectory, route and station basic information, and weather data, bus passenger flow data was reconstructed. The K-medians algorithm was used to divide passengers into commuter and non-commuter categories. Taking the factors of passenger type, historical passenger flow, time period, high/flat peak, week, precipitation, and major events as input vectors, a single model of CNN and GRU was established, and forecasts were conducted using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) as evaluation indicators. As a single model is not suitable for multi-feature time series forecasting, line passenger flow and cross-section passenger flow prediction models combined with a CNN and GRU were constructed. Taking Beijing Special 15 Bus as an example, the classified passenger flows of routes and cross-sections under the scenarios of working days and non-working days were predicted. Analysis results show that for commuter and non-commuter routes and cross-section passenger flows, the MSEs of the combined model reduce by 57.932, 13.106, and 33.987 on average, the RMSEs reduce by 1.862, 1.058, and 1.538 on average, and the MAEs reduce by 1.399, 0.487, and 0.613 on average, respectively. Thus, the CNN-GRU combined model driven by multi-source data has a good prediction performance. 3 tabs, 7 figs, 36 refs.
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图 6 第2类客流预测结果对比
Figure 6. Forecast results comparison of second type of passenger flow
图 7 最大断面客流预测结果对比
Figure 7. Forecast results comparison of passenger flow at largest cross-section
表 1 原始刷卡数据字段名
Table 1. Field name of original swipe data
序号 字段名 中文名 具体说明 1 GRAND_CARD_CODE 一卡通卡号 2 LINE_CODE 线路编号 3 ON_STATION 上车站点 4 OFF_STATION 下车站点 5 DEAL_TIME 交易时间 第2次刷卡时间 6 DEAL_TYPE 交易类型 06为正常交易 7 CARD_TYPE 卡类型 1为普通卡；
18为老人卡；
19为学生卡等8 RUN_COMP_CODE 公交运行公司编号 9 VEHICLE_CODE 车辆编号 10 DRIVER_CODE 驾驶人编号 
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表 2 公交客流主要影响因素
Table 2. Main influencing factors of bus passenger flow
变量 影响因素 数据类型 数据范围 B1 乘客类型 数值型 依据客流分类方案定 B2 降水量 降水量 B3 历史客流量 依据预测时间粒度选用不同历史客流量 B4 时段 一天24 h B5 高/平峰 早平峰为0；早高峰为1；午平峰为2；晚高峰为3；晚平峰为4 B6 星期 1、2、…、7 B7 重大活动与突发事件 客流聚集为0；客流疏散为1
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表 3 ARIMA、CNN、GRU、CNN-GRU模型预测误差
Table 3. Prediction errors of ARIMA, CNN, GRU, CNN-GRU models
短时公交客流 模型 MSE RMSE MAE 未分类客流 ARIMA模型[36] 573.294 23.944 16.537 CNN模型 456.194 21.359 13.375 GRU模型 422.867 20.564 12.986 CNN-GRU组合模型 324.453 18.013 10.734 第1类客流 ARIMA模型 365.236 19.111 13.963 CNN模型 282.373 16.804 10.290 GRU模型 259.577 16.111 9.975 CNN-GRU组合模型 213.043 14.596 8.734 第2类客流 ARIMA模型 61.205 7.823 4.341 CNN模型 46.448 6.815 3.169 GRU模型 43.735 6.613 3.403 CNN-GRU组合模型 31.986 5.656 2.799 最大断面客流 ARIMA模型 261.279 16.164 9.824 CNN模型 144.721 12.030 7.296 GRU模型 133.987 11.575 6.862 CNN-GRU组合模型 105.367 10.265 6.466
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