Influence of Location Frequency on Travel Mode Extraction Using Cellular Phone Data
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摘要:
作为影响手机信令数据定位质量的关键因素,定位频率对交通方式的识别精度具有重要影响. 为量化定位频率与交通方式识别精度之间的变化规律,首先,提出一种基于随机森林的交通方式识别模型;其次,在通信运营商的协助下,通过开展实地数据采集实验,完成手机信令数据及对应真实出行信息的同步采集,并利用该数据集对本文提出的交通方式识别模型进行验证;最后,通过数据抽样形成一系列拥有不同定位频率的手机信令数据集,利用该系列数据集对不同定位频率下的交通方式识别精度进行评估研究. 研究结果表明:本文模型对步行、非机动车、汽车和公共交通4种交通方式的总体识别准确率为79.2%;每种交通方式对定位频率的敏感性不同,其中非机动车与公交的敏感性更高,步行和汽车的敏感性相对较低;随着平均定位频率从48 s/条下降至241 s/条,非机动车和公交的整体识别精度下降幅度分别约为19.2%和21.5%,而步行与汽车的整体识别精度则分别下降12.8%与11.5%;综合考虑识别准确率与计算效率两方面的需求,建议将60 s/条作为用户筛选与数据抽样的最佳阈值.
Abstract:As a key factor affecting location quality of cellular phone data, location frequency has an important influence on the extraction accuracy of travel mode. In order to quantify the change rule between the location frequency and accuracy of travel mode extraction, a travel mode extraction model based on random forest is proposed. Second, with the help of communication operators, through a field data collection, individual cellular phone data and corresponding real travel information were simultaneously acquired. The dataset is used to verify the travel mode extraction model. Finally, a series of cellular phone datasets with different location frequencies are built through data sampling. With this series of datasets, the extraction accuracy of traffic modes under different location frequencies is evaluated. The evaluation results show that the overall extraction accuracy for walking, non-motorized vehicles, cars, and buses is 79.2%, and the sensitivity of each travel mode to location frequency is different. The sensitivity of non-motorized vehicles and buses is higher, and the sensitivity of walking and cars is relatively low. As the location frequency is decreased from 48 s to 241 s, the overall accuracy of non-motorize vehicles and buses is decreased by 19.2% and 21.5%, respectively, while that of walking and car is decreased by 12.8% and 11.5%, respectively. Owning to the requirements of extraction accuracy and computing efficiency, 60 seconds is recommended as the optimal threshold for user screening and data sampling.
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Key words:
- intelligent traffic /
- travel mode /
- cellular phone data /
- location frequency /
- random forest
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表 1 手机信令数据样例
Table 1. Sample of cellular phone data
用户全球标识码 设备标识码 位置区编号 基站小区编号 460***340 2185 ***7347 34054 1710732 460***340 2185 ***7347 34054 1710732 460***340 2185 ***7347 34054 1678945 日期 时刻 基站经度/° 基站纬度/° 2019-9-21 9:00:34 106.6992 26.58389 2019-9-21 9:01:41 106.7025 26.58639 2019-9-21 9:02:10 106.7025 26.58639 表 2 本研究使用的出行数据集构成
Table 2. Composition of dataset of interest
交通方式 数据量/条 出行段量/个 步行 12412 114 非机动车 9534 77 汽车 23655 207 公共交通 23458 186 合计 69059 584 表 3 特征参数的重要度排名
Table 3. Characteristic parameters ranking in terms of importance
变量 变量意义 重要度/% f 基站使用频率 10.02 Z11 11 min 时间窗直线距离 8.45 Ttotal 出行总时间 7.92 DOD 出行OD距离 7.30 Z9 9 min 时间窗直线距离 7.26 VaveOD OD间平均速度 6.96 n 基站使用个数 6.36 Z7 7 min 时间窗直线距离 5.23 Z5 5 min 时间窗直线距离 5.16 $ V_{\mathrm{ave}Z_{11}} $ 11 min 时间窗直线平均速度 4.04 $ V_{\mathrm{ave}Z_9} $ 9 min 时间窗直线平均速度 3.54 $ V_{\mathrm{ave}Z_7} $ 7 min 时间窗直线平均速度 3.51 $ V_{\mathrm{ave}Z_5} $ 5 min 时间窗直线平均速度 2.98 L11 11 min 时间窗累积距离 2.97 L9 9 min 时间窗累积距离 2.54 $ V_{\mathrm{ave}L_{11}} $ 11 min 时间窗累积平均速度 2.44 $ V_{\mathrm{ave}L_9} $ 9 min 时间窗累积平均速度 2.40 L7 7 min 时间窗累积距离 2.28 $ V_{\mathrm{ave}L_7} $ 7 min 时间窗累积平均速度 2.08 L5 5 min 时间窗累积距离 1.71 $ V_{\mathrm{ave}L_5} $ 5 min 时间窗累积平均速度 1.50 Tb 相邻数据的时间差 1.26 Db 相邻数据的基站切换距离 1.10 Vb 相邻数据的基站切换速度 1.02 表 4 机器学习算法主要参数
Table 4. Major parameters in machine learning algorithm
算法 参数设置 参数值 支持向量机 核函数 径向基函数 核参数 σ 0.25 惩罚系数 1 BP神经网络 神经元层数 2 神经元个数 (100,50) 隐藏层激活函数 Relu 权重优化算法 Sgd 初始学习率 Adaptive 表 5 测试集识别结果
Table 5. Extraction results of test dataset
交通方式 出行段数量/个 识别结果/个 步行 非机动 公交车 汽车 步行 37 33 1 2 1 非机动车 24 2 19 3 0 公共交通 65 0 8 46 10 汽车 58 0 2 9 47 合计 184 35 30 60 58 表 6 评价指标统计结果
Table 6. Statistical results of evaluation indicators
交通方式 出行段数量/个 P/% R/% Fscore/% 步行 37 94.3 89.2 91.7 非机动车 24 63.3 79.2 70.4 公共交通 65 76.7 71.9 74.2 汽车 58 81.0 81.0 81.0 合计 184 79.2 79.2 79.2 -
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