.png){kind=icon}
返回
High-Frequency Trajectory Map Matching Algorithm Based on Road Network Topology
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2022.3155689.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurately mapping the raw global position system (GPS) trajectories to the road network is the basis for studying the application of trajectory data. This study proposes a novel off-line map matching algorithm based on road network topology, to address the problems of low execution efficiency and poor matching accuracy of selective look-ahead map matching (SLAMM) algorithm. First, the noise points of the trajectory data are removed by data preprocessing. Second, the algorithm searches for critical samples in the trajectory data and segments the data accordingly. Then, the adjacent road segments around the transition node corresponding to the critical sample are selected as candidate arcs. Finally, the segmented trajectory data are matched to the road network by constructing an error ellipse. The algorithm fully considers the topology of the road network and the characteristics of high-frequency trajectory data. The experimental results, using Beijing trajectory data to perform matching on an actual road network environment, show that the proposed algorithm is more efficient and robust than other map matching algorithms for high-frequency trajectories.
Keyword:
Trajectory
Roads
Hidden Markov models
Network topology
Global Positioning System
Topology
Clustering algorithms
High-frequency trajectory
map matching
road network topology
vehicle trajectory
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
机构
引用论文
Online Map-Matching of Noisy and Sparse Location Data With Hidden Markov and Route Choice Models
PREAI
分享收藏Validation of yield-enhancing quantitative trait loci from a low-yielding wild ancestor of rice
PREAI
分享收藏Survey on call admission control protocols in cellular networks.png)
Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing
Proceedings of the Second International Conference on Internet of things, Data and Cloud ComputingIF0
PREAI
分享收藏Spatial and Temporal Variations in Indoor Environmental Conditions, Human Occupancy, and Operational Characteristics in a New Hospital BuildingPLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Recognizing Network Trip Patterns Using a Spatio-Temporal Vehicle Trajectory Clustering Algorithm
PREAI
分享收藏An Improved Map-Matching Technique Based on the Frechet Distance Approach for Pedestrian Navigation ServicesSENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容