首页|非机动车超越行为轨迹分段识别与分析方法

非机动车超越行为轨迹分段识别与分析方法

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
非机动车超越次数是非机动车道服务水平评价的重要参数,随着路侧高位视频设备的应用普及,为非机动车超越行为识别提供了良好的视频数据来源.目前有关轨迹分段方法的研究难以适应非机动车行驶角度变化频繁且超越轨迹较长的特点.本文提出基于片段轨迹时长和分割间隔时间两个参数的轨迹分段方法,根据视频获取的640条超越轨迹数据,分析非机动车各类超越行为的特性,选取纵向平均速度、纵向速度标准差、横向平均速度、横向速度标准差作为特征参数,基于K最近邻(K-Nearest Neighbor,KNN)算法建立非机动车超越分类模型.根据超越行为持续时间平均值确定自行车片段轨迹时长取值为[4,9]s,分割间隔时间取值为[1,8]s;电动自行车的片段轨迹时长取值为[4,8]s,分割间隔时间取值为[1,7]s,利用分类模型对各参数组合分段获得的非机动车轨迹片段进行识别.研究结果表明:自行车片段轨迹时长为8s,分割间隔时间为6s;电动自行车片段轨迹时长为6s,分割间隔时间为4s时,对应的识别误差百分比最低,分段效果最佳.
A Trajectory Segmentation and Analysis Method for Non-motor Vehicle Overtaking Behavior Recognition
The number of overtaking by non-motor vehicles is crucial for evaluating the level of services.With the widespread availability of roadside video equipment,it provides valuable video data for recognizing non-motor vehicle overtaking behavior.However,existing research on trajectory segmentation is difficult to adapt to the frequent changes in driving angles and lengthy overtaking trajectories exhibited by non-motor vehicles.In this paper,non-motor vehicle trajectories are segmented into fixed-length segments(trajectory segment length)based on specific interval time(segmentation interval).Using data from 640 overtaking trajectories captured from videos,various overtaking behaviors of non-motor vehicles are analyzed.Key characteristic parameters such as average longitudinal velocity,longitudinal velocity standard deviation,average lateral velocity,and lateral velocity standard deviation are selected.A classification model is then established using the K-Nearest Neighbor(KNN)algorithm to classify non-motor vehicle overtaking behaviors.Based on the average duration of overtaking behaviors,trajectory segment lengths for bicycles range from 4 to 9 seconds with segmentation intervals from 1 to 8 seconds,while for electric bicycles,trajectory segment lengths range from 4 to 8 seconds with segmentation intervals from 1 to 7 seconds.The classification model is then employed to identify overtaking behavior in non-motor vehicle trajectory segments for each parameter combination.Results indicate that for bicycles,a trajectory segment length of 8 seconds and a segmentation interval of 6 seconds yield the lowest recognition error rate and the most effective segmentation.Similarly,for electric bicycles,a trajectory segment length of 6 seconds and a segmentation interval of 4 seconds achieve optimal performance.

urban trafficovertaking behavior recognitiontrajectory segmentationnon-motor vehicletrajectory segment lengthsegmentation interval

张蕊、段予、孔令争、侯先磊

展开 >

北京建筑大学,土木与交通工程学院,北京 100044

中冶京诚工程技术有限公司,北京 100176

城市交通 超越行为识别 轨迹分段 非机动车 片段轨迹时长 分割间隔时间

国家重点研发计划住建部软科学研究计划北京市市属高等学校高水平科研创新团队建设支持计划

2022YFB26019002018-R2-046BPHR20220109

2024

10.16097/j.cnki.1009-6744.2024.03.031

交通运输系统工程与信息
中国系统工程学会

交通运输系统工程与信息

CSTPCD北大核心
影响因子：0.664
ISSN：1009-6744
年,卷(期)：2024.24(3)
  • 11