An Intersection-First Approach for Road Network Generation from Crowd-Sourced Vehicle Trajectories
An Intersection-First Approach for Road Network Generation from Crowd-Sourced Vehicle Trajectories
复制标题
根据众包车辆轨迹生成道路网络的交叉口优先方法
DOI：
10.3390/ijgi8110473
复制
发表时间：
2019-10
影响因子：
3.4
通讯作者：
Wang Dehao
中科院分区：
文献类型：
--
作者：
Zhang Caili;Xiang Longgang;Li Siyu;Wang Dehao
从众包的车辆轨迹数据中提取高度详细和准确的道路网络信息，具有成本低，更新速度快的优点，是一个热门话题。随着无线传输技术、空间定位技术的快速发展，以及软硬件计算能力的提高，全球定位系统（GPS）轨迹的分析和道路信息的提取越来越受到研究者的关注。道路交叉口是道路的重要组成部分，在导航和城市规划中起着重要作用。尽管对该主题已有许多研究，但确定道路交叉口仍然具有挑战性，特别是对于准确度较低、采样频率较低且分布不均匀的众包车辆轨迹数据。因此，我们提供了一个新的交叉口优先的道路网络生成方法的基础上，低频出租车轨迹。首先分别从矢量空间和栅格空间采用不同的方法提取道路交叉口，然后提出一种综合识别策略，融合不同方案的交叉口提取结果，以克服车辆轨迹采样的稀疏性和分布的不均匀性;最后，我们调整道路信息，修复断裂路段，并根据相交结果提取路网的单双向信息和转弯关系，保证路网的几何精确和拓扑正确。与其他方法相比，该方法在视觉检查和定量比较方面都显示出更好的结果。该方法可以解决上述问题，保证道路交叉口和道路网络的完整性和准确性。因此，所提出的方法提供了一个有前途的解决方案，丰富和更新导航道路网络，并可以应用于智能交通系统。
Extracting highly detailed and accurate road network information from crowd-sourced vehicle trajectory data, which has the advantages of being low cost and able to update fast, is a hot topic. With the rapid development of wireless transmission technology, spatial positioning technology, and the improvement of software and hardware computing ability, more and more researchers are focusing on the analysis of Global Positioning System (GPS) trajectories and the extraction of road information. Road intersections are an important component of roads, as they play a significant role in navigation and urban planning. Even though there have been many studies on this subject, it remains challenging to determine road intersections, especially for crowd-sourced vehicle trajectory data with lower accuracy, lower sampling frequency, and uneven distribution. Therefore, we provided a new intersection-first approach for road network generation based on low-frequency taxi trajectories. Firstly, road intersections from vector space and raster space were extracted respectively via using different methods; then, we presented an integrated identification strategy to fuse the intersection extraction results from different schemes to overcome the sparseness of vehicle trajectory sampling and its uneven distribution; finally, we adjusted road information, repaired fractured segments, and extracted the single/double direction information and the turning relationships of the road network based on the intersection results, to guarantee precise geometry and correct topology for the road networks. Compared with other methods, this method shows better results, both in terms of their visual inspections and quantitative comparisons. This approach can solve the problems mentioned above and ensure the integrity and accuracy of road intersections and road networks. Therefore, the proposed method provides a promising solution for enriching and updating navigable road networks and can be applied in intelligent transportation systems.
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影响因子：
2.5
作者：
Luliang Tang;Zihan Kan;Xia Zhang;Xue Yang;Fangzhen Huang;Qingquan Li
通讯作者：
Luliang Tang;Zihan Kan;Xia Zhang;Xue Yang;Fangzhen Huang;Qingquan Li
DOI：
10.1007/3-540-36477-3_10
发表时间：
2003
期刊：
--
影响因子：
--
作者：
S. Edelkamp;Stefan Schrödl
通讯作者：
S. Edelkamp;Stefan Schrödl
DOI：
10.3390/ijgi6010001
发表时间：
2016-12
期刊：
ISPRS Int. J. Geo Inf.
影响因子：
--
作者：
Xingzhe Xie;Wenzi Liao;H. Aghajan;P. Veelaert;W. Philips
通讯作者：
Xingzhe Xie;Wenzi Liao;H. Aghajan;P. Veelaert;W. Philips
影响因子：
1.6
作者：
Davies, Jonathan J.;Beresford, Alastair R.;Hopper, Andy
通讯作者：
Hopper, Andy
影响因子：
8.2
作者：
Xiangyun Hu;Yijing Li;J. Shan;Jianqing Zhang;Yongjun Zhang
通讯作者：
Xiangyun Hu;Yijing Li;J. Shan;Jianqing Zhang;Yongjun Zhang