SRMM: Simple Real-Time Map-Matching Model and Algorithm in Urban Area Traffic Integrated Application

2010 ◽  
Author(s):  
Zhenxing Zhu ◽  
Jianping Xing ◽  
Lingguo Meng ◽  
Yubing Wu ◽  
Feng Li
Keyword(s):  
Real Time ◽  
Urban Area ◽  
Map Matching ◽  
Matching Model ◽  
Time Map

Algorithm on real-time map-matching of multi-weight probability

2013 ◽  
Vol 26 (2) ◽  
pp. 166-170
Author(s):  
Yang Li ◽  
Xiaodong Zhang ◽  
Yuanlv Bao
Keyword(s):  
Real Time ◽  
Map Matching ◽  
Time Map

Research on parallelized real-time map matching algorithm for massive GPS data

2017 ◽  
Vol 20 (2) ◽  
pp. 1123-1134 ◽  
Author(s):  
Hongyu Wang ◽  
Jin Li ◽  
Zhenshan Hou ◽  
Ruochen Fang ◽  
Wenbo Mei ◽  
...  
Keyword(s):  
Real Time ◽  
Gps Data ◽  
Map Matching ◽  
Matching Algorithm ◽  
Time Map

An enhanced weight-based real-time map matching algorithm for complex urban networks

2019 ◽  
Vol 534 ◽  
pp. 122318 ◽  
Author(s):  
Mujun He ◽  
Linjiang Zheng ◽  
Wei Cao ◽  
Jing Huang ◽  
Xu Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Map Matching ◽  
Matching Algorithm ◽  
Urban Networks ◽  
Time Map

Accurate Real-time Map Matching for Challenging Environments

2017 ◽  
Vol 18 (4) ◽  
pp. 847-857 ◽  
Author(s):  
Reham Mohamed ◽  
Heba Aly ◽  
Moustafa Youssef
Keyword(s):  
Real Time ◽  
Map Matching ◽  
Time Map

Online Learning for Accurate Real-Time Map Matching

2016 ◽  
pp. 67-78 ◽  
Author(s):  
Biwei Liang ◽  
Tengjiao Wang ◽  
Shun Li ◽  
Wei Chen ◽  
Hongyan Li ◽  
...  
Keyword(s):  
Online Learning ◽  
Real Time ◽  
Map Matching ◽  
Time Map

scholarly journals Real-Time Map Matching: A New Algorithm Integrating Spatio-Temporal Proximity and Improved Weighted Circle

2019 ◽  
Vol 11 (1) ◽  
pp. 288-297 ◽  
Author(s):  
Wenxin Teng ◽  
Yanhui Wang
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Navigation Systems ◽  
Map Matching ◽  
Test Results ◽  
Matching Accuracy ◽  
Optimal Matching ◽  
Temporal Proximity ◽  
Time Map ◽  
Spatio Temporal

Abstract Previous real-time map matching algorithms for in-vehicle navigation systems had some efficiencies and defects on time lagging and low accuracy. As a response, this paper proposes a new algorithm that integrates STP (spatio-temporal proximity) and IWC (improved weighted circle), in which the new algorithm proposes STP to dynamically refine candidate matching roads, and IWC to adaptively identify the optimal matching road. Specifically, three spatio-temporal proximity indicators are defined in STP to build a three-dimensional stereoscopic cone, and then the two-dimensional projection of the cone are adopted to dynamically select the candidate matching roads. Further, by adaptively setting the weight, the IWC algorithm is developed to integrate three new parameters to adaptively determine the optimal matching road. The test results show that the matching accuracy of the algorithm is over 95%, much higher than that of the existing algorithm, which demonstrates the feasibility and efficiency of the new algorithm.

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