共查询到19条相似文献，搜索用时 43 毫秒
1.
基于GPS轨迹数据的地图匹配算法 总被引：6，自引：0，他引：6
针对GPS浮动车轨迹数据具有整体运动趋势的特点,结合城市路网行车限制的约束,提出一种GPS轨迹数据的全局地图匹配方法,综合考虑轨迹曲线与路网路径的曲线相似性、实际行车的路段几何拓扑和交通管制约束下的连通性,实现较好的地图匹配效果,并通过实验进行验证,为GPS浮动车数据的进一步分析应用打下基础。 相似文献
2.
着眼于低频浮动车轨迹数据,对地图匹配问题进行了抽象,并分析了影响匹配结果的几何约束与拓扑约束。针对GPS采样的低频性和城市路网的复杂性,提出了一种路网拓扑约束下的增量型地图匹配算法（topology-constrained incremental matching algorithm,TIM）。选取北京市浮动车的GPS样例轨迹数据进行匹配,结果表明,该匹配算法在不同复杂程度的城市路网下均表现较好。 相似文献
3.
4.
浮动车地图匹配算法研究 总被引：3，自引：0，他引：3
针对现有浮动车地图匹配算法应用于城市复杂路网时面临的关键技术难点,本文基于浮动车数据,在 SuperMap GIS 平台下实现了城市交通路网的构建,并研究了一种浮动车地图匹配的新算法：基于网格的候选路段确定,基于距离、航向、可达性权重的定位点匹配及基于最短路径的行驶轨迹选择。算法能够满足浮动车地图匹配准确性与实时性的要求,为获取城市道路的交通拥堵状况信息提供可靠依据。 相似文献
5.
6.
7.
8.
9.
浮动车轨迹数据具有覆盖范围广、更新周期短、获取成本低等特点，对于地图的生产和更新具有重要意义，但是由于受到卫星信号被遮挡及多路径效应的影响，其精度普遍较低。本文采用一种基于OSM作为参考数据的方式对浮动车轨迹数据进行校正。首先通过一种分层时空地图匹配的方式将轨迹数据与OSM进行匹配；然后采用引力模型对数据进行校正；最后在武汉市出租车轨迹数据上进行了试验。结果表明，本文提出的数据校正方法可以有效地提高浮动车轨迹数据的精度。 相似文献
10.
针对精度差、频率低的浮动车数据特点,给出了空间和拓扑约束下的最短路径浮动车数据地图匹配算法,基于不同采样频率的匹配结果证明算法准确度高。基于武汉市浮动车数据的匹配结果表明,算法具有高可靠性,可以用于浮动车数据的交通信息提取与特征挖掘。 相似文献
11.
Accurate vehicle tracking is essential for navigation systems to function correctly. Unfortunately, GPS data is still plagued with errors that frequently produce inaccurate trajectories. Research in map matching algorithms focuses on how to efficiently match GPS tracking data to the underlying road network. This article presents an innovative map matching algorithm that considers the trajectory of the data rather than merely the current position as in the typical map matching case. Instead of computing the precise angle which is traditionally used, a discrete eight-direction chain code, to represent a trend of movement, is used. Coupled with distance information, map matching decisions are made by comparing the differences between trajectories representing the road segments and GPS tracking data chain-codes. Moreover, to contrast the performance of the chain-code algorithm, two evaluation strategies, linear and non-linear, are analyzed. The presented chain-code map matching algorithm was evaluated for wheelchair navigation using university campus sidewalk data. The evaluation results indicate that the algorithm is efficient in terms of accuracy and computational time. 相似文献
12.
介绍了流动车数据的概念,给出了处理流动车数据基本流程。给出了顾及历史定位数据的流动车数据地图匹配算法,提出了在一定准则下的路段速度匹配的方法,并根据现场检验结果分析了方法的有效性。随后给出了基于网络地理信息系统构建道路拥挤状况实时发布系统的框架。 相似文献
13.
14.
针对目前的地图匹配算法普遍只能修正垂直道路方向的GPS定位误差,而对道路延伸方向的定位误差修正方法研究很少,仅有的研究成果应用又不理想的问题,本文通过定量分析GPS速度与定位误差的关系,设计了一种GPS位置修正基准的确定方法,据此设计了一种基于GPS独立定位的地图匹配算法,重点用于修正道路延伸方向的GPS定位误差。运用某大城市的实测GPS数据,进行了上述地图匹配算法的验证。结果表明,相比现有算法,利用文中设计的地图匹配算法获得的GPS定位精度明显有所提高,从而可为GPS数据用户提供更高质量的信息基础。 相似文献
15.
With the increasing availability of location-aware devices, passively collected big GPS trajectory data offer new opportunities for analyzing human mobility. Processing big GPS trajectory data, especially extracting information from billions of trajectory points and assigning information to corresponding road segments in road networks, is a challenging but necessary task for researchers to take full advantage of big data. In this research, we propose an Apache Spark and Sedona-based computing framework that is capable of estimating traffic speeds for statewide road networks from GPS trajectory data. Taking advantage of spatial resilient distributed datasets supported by Sedona, the framework provides high computing efficiency while using affordable computing resources for map matching and waypoint gap filling. Using a mobility dataset of 126 million trajectory points collected in California, and a road network inclusive of all road types, we computed hourly speed estimates for approximately 600,000 segments across the state. Comparing speed estimates for freeway segments with speed limits, our speed estimates showed that speeding on freeways occurred mostly during the nighttime, while analysis of travel on residential roads showed that speeds were relatively stable over the 24-h period. 相似文献
16.
17.
In transportation, the trajectory data generated by various mobile vehicles equipped with GPS modules are essential for traffic information mining. However, collecting trajectory data is susceptible to various factors, resulting in the lack and even error of the data. Missing trajectory data could not correctly reflect the actual situation and also affect the subsequent research work related to the trajectory. Although increasing efforts are paid to restore missing trajectory data, it still faces many challenges: (1) the difficulty of data restoration because traffic trajectories are unstructured spatiotemporal data and show complex patterns; and (2) the difficulty of improving trajectory restoration efficiency because traditional trajectory interpolation is computationally arduous. To address these issues, a novel road network constrained spatiotemporal interpolation model, namely Traj2Traj, is proposed in this work to restore the missing traffic trajectory data. The model is constructed with a seq2seq network and integrates a potential factor module to extend environmental factors. Significantly, the model uses a spatiotemporal attention mechanism with the road network constraint to mine the latent information in time and space dimensions from massive trajectory data. The Traj2Traj model completes the road-level restoration according to the entire trajectory information. We present the first attempt to omit the map-matching task when the trajectory is restored to solve the time-consuming problem of map matching. Extensive experiments conducted on the provincial vehicle GPS data sets from April 2018 to June 2018 provided by the Fujian Provincial Department of Transportation show that the Traj2Traj model outperforms the state-of-the-art models. 相似文献
18.
Mobile user identification aims at matching different mobile devices of the same user using trajectory data, which has attracted extensive research in recent years. Most of the previous work extracted trajectory features based on regular grids, which will lead to incorrect feature representation due to lack of geographic information. Besides, most trajectory similarity models only considered one single distance measure to calculate the similarity between users, which ignore the connection between different distance measures and may lead to some false matches. In light of this, we present a novel user identification method based on road networks and multiple distance measures in this article. The proposed method segments a city map into several grids and road segments based on road networks. Then it extracts location and road information of trajectories to jointly construct user features. Multiple distance measures are fused by a discriminant model to improve the effect of user identification. Experiments on real GPS trajectory datasets show that our proposed method outperforms related similarity measure methods and is stable for mobile user identification. Meanwhile, our method can also achieve good identification results even on sparse trajectory datasets. 相似文献
19.
车辆导航系统中定位数据处理和地图匹配技术 总被引：11，自引：1，他引：11
分析了GPS误差来源，并提出了相应的处理办法；融合电子地图中的道路数据和GPS所提供的定位数据的地图匹配算法，可以有效地提高车辆导航的定位精度。在分析各种地图匹配算法基础上，提出了一种实用的地图匹配方法，并且在实践中得到了验证。 相似文献