| 基于粒子滤波的行车轨迹路网匹配方法 |
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| 引用本文： | 郑诗晨,盛业华,吕海洋.基于粒子滤波的行车轨迹路网匹配方法[J].地球信息科学,2020,22(11):2109-2117. |
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| 作者姓名： | 郑诗晨 盛业华 吕海洋 |
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| 作者单位： | 1.南京师范大学虚拟地理环境教育部重点实验室,南京 2100232.江苏省地理信息资源开发与利用协同创新中心,南京 2100233.南京邮电大学江苏省智慧健康大数据分析与位置服务工程实验室,南京 210023 |
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| 基金项目： | 国家自然科学基金重点项目(41631175) |
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| 摘 要： | 行车轨迹是一种时间序列的地理空间位置采样数据,而传统的轨迹—路网匹配方法主要以全局或局部寻优的方式建立轨迹—路网匹配关系,影响了时空场景中数据的匹配计算过程的相对独立性。针对这个问题,本文基于粒子滤波（Particle Filter,PF）原理建立行车轨迹与道路网络之间的匹配关系。首先,沿轨迹中车辆运动方向在道路网络中搜索邻近道路节点,在与道路节点拓扑邻接的道路弧段上初始化随机生成粒子,根据轨迹中车辆运动模型将粒子沿所在道路弧段移动;然后,基于PF原理计算各时刻粒子运动状态及与行车轨迹采样点之间的距离误差,根据高斯概率密度函数计算粒子权重并利用随机重采样方法进行粒子重采样,迭代更新粒子运动状态;最后,计算与搜索到的道路节点拓扑邻接的每条道路弧段中累计粒子权重,通过各道路弧段累计权重计算轨迹—路网匹配关系。以行车轨迹进行实验表明,利用本文方法可以通过粒子时空变化反映采样点的移动,行车轨迹—路网匹配结果的正确率大于85%,能够实现行车轨迹和路网的准确匹配。
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| 关 键 词： | 行车轨迹 时间序列 粒子滤波 道路网络 地图匹配 随机粒子 邻接道路弧段 运动模型 |
| 收稿时间： | 2019-11-27 |
Vehicle Trajectory-map Matching based on Particle Filter |
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| ZHENG Shichen,SHENG Yehua,LV Haiyang.Vehicle Trajectory-map Matching based on Particle Filter[J].Geo-information Science,2020,22(11):2109-2117. |
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| Authors: | ZHENG Shichen SHENG Yehua LV Haiyang |
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| Institution: | 1. Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China3. Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China |
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| Abstract: | Vehicle trajectory is a time series geospatial location sampling data. The traditional vehicle trajectory-map matching methods are mainly computed by ways of global or local incremental optimization, which limited the relative independence in matching process of the trajectory data in spatial temporal situation. To address this problem, this paper proposes the method of computing matching relationships between vehicle trajectory and road map based on the Particle Filter (PF) method. First, construct the road network from the road dataset, and search the neighboring nodes from the road network based on the vehicle sampling locations along the moving direction that are detected from the vehicle trajectory. Then, construct the motion model based on the vehicle trajectories, randomly generate particles on the road arcs that are related to the searched nodes, and move the particles along the sampled road segments according to the trajectory motion model. Second, compute the motion states of the particles according to the motion model in each time state, get the distance errors between the particles and the vehicle position sampling locations, obtain the particle weights based on the Gaussian probability density function, resample particles based on the random resampling method, and then update the motion states of particles iteratively. Finally, compute the accumulated weights of the particles in each of the topologically related road arcs, which are searched by the neighboring nodes, and calculate the matching relations between the vehicle trajectories and the map based on the accumulated weights of the particles. With this method, the experiments were conducted based on the vehicles' trajectories, which were two long sequenced trajectories with the total length > 102 km. The results showed that 85.51% and 93.01% correctness rates of vehicle trajectory-map matching experiments had been achieved for each of the vehicle trajectories. Besides, the motion of the vehicle sampling locations could be reflected by the spatial-temporal movements of the particles, where particles started to follow the motion of the vehicle sampling locations after a few time states. The results showed that it could achieve the accurate matching relations between the vehicle trajectories and the road map. |
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| Keywords: | vehicle trajectory time series particle filter road network map matching random particles related road arcs motion model |
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