地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (4): 754-765.doi: 10.12082/dqxxkx.2023.220414
庄汉宸1,2(), 张亚茹1,2, 王文轩1,2, 张书亮1,2,*(
)
收稿日期:
2022-06-16
修回日期:
2022-08-19
出版日期:
2023-04-25
发布日期:
2023-04-19
通讯作者:
*张书亮(1974—),男,河南南阳人,博士,教授,博士生导师,主要从事时空大数据分析、城市内涝地理建模与模 拟研究。E-mail: zhangshuliang@njnu.edu.cn作者简介:
庄汉宸(1999—),男,浙江舟山人,本科生,主要从事时空数据挖掘研究。E-mail: zhuanghanchen@163.com
基金资助:
ZHUANG Hanchen1,2(), ZHANG Yaru1,2, WANG Wenxuan1,2, ZHANG Shuliang1,2,*(
)
Received:
2022-06-16
Revised:
2022-08-19
Online:
2023-04-25
Published:
2023-04-19
Contact:
ZHANG Shuliang
Supported by:
摘要:
渣土车轨迹数据蕴含丰富的行为模式信息,包括停车行为、运输路径、异常活动、渣土装载与倾倒OD点等关键特征,已逐渐成为渣土车运行监测与作业行为监管的主要数据来源。但是目前在渣土车作业行为提取中仍主要采用车载GPS数据结合工地电子围栏的传统签到系统,存在电子围栏与道路相互包含、重叠等一系列问题。针对传统渣土车作业行为提取中存在的车辆作业误判问题,本文提出一种顾及轨迹还原与分类的渣土车作业行为提取方法。① 从运动状态和几何形态2个方面理解并识别渣土车作业行为模式;② 利用顾及时间与距离的停留点提取算法提取车辆停留点,处理停留点与轨迹的映射关系,完成基于停留点的轨迹匹配;③ 构建平均相似值函数对轨迹进行语义信息增强;④ 提出SR-LGBM算法,筛选作业轨迹与非作业轨迹,实现渣土车的作业行为提取。采用南京市渣土车轨迹数据进行测试,结果表明,本文方法的准确率达97.29%,相比GaussianNB、Logistic Regression等传统分类算法其准确率与召回率均得到不同程度的提高,有效解决了电子围栏与道路重叠或多个围栏交叉造成的误判问题,可实现准确、高效的作业行为提取。
庄汉宸, 张亚茹, 王文轩, 张书亮. 顾及轨迹还原与分类的渣土车作业行为提取方法[J]. 地球信息科学学报, 2023, 25(4): 754-765.DOI:10.12082/dqxxkx.2023.220414
ZHUANG Hanchen, ZHANG Yaru, WANG Wenxuan, ZHANG Shuliang. Extraction of Muck Truck Operation Behavior Considering Trajectory Restoration and Classification[J]. Journal of Geo-information Science, 2023, 25(4): 754-765.DOI:10.12082/dqxxkx.2023.220414
表2
SR-LGBM算法的主要参数
参数 | 值 | 解释 |
---|---|---|
kernel | linear | SVM核函数类型,本文采用线性核函数 |
C | 0.1 | SVM惩罚系数,用于调整准确率和泛化能力 |
num_leaves | 255 | 每棵树的叶子数量 |
max_bin | 255 | 表示最大的桶的数量,能根据此值来自动压缩内存 |
max_depth | no limit | 描述了树的最大深度,能够处理模型的过拟合 |
min_child_samples | 20 | 一片叶子需具有的最小记录数,用来处理过度拟合的问题 |
feature_fraction | 0.8 | 在每次迭代中随机选择用于构建树的特征的部分 |
bagging_fraction | 0.8 | 每次迭代要使用的数据,一般用于加快训练和避免过度拟合 |
表5
不同算法的渣土车作业行为分类结果比较
算法 | 准确率 | 精确率 | 召回率 | F1值 |
---|---|---|---|---|
GaussianNB | 89.40 | 90.11 | 96.06 | 92.99 |
k-Nearest Neighbors | 86.43 | 92.40 | 88.76 | 90.54 |
Logistic Regression | 92.45 | 95.85 | 93.74 | 94.79 |
Multi-layer Perceptron | 92.54 | 96.86 | 92.82 | 94.79 |
RBF-SVM | 88.80 | 89.17 | 96.41 | 92.65 |
LGBM | 96.01 | 97.43 | 97.32 | 97.37 |
SR-LGBM | 97.29 | 98.17 | 98.17 | 98.17 |
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