Vehicle trajectory map-matching method based on improved multi-weight theory

2021 ◽  
Author(s):  
Yi Yuan ◽  
Guangwu Chen
Keyword(s):  
Map Matching ◽  
Matching Method ◽  
Vehicle Trajectory

Vehicle Trajectory Enhanced Map-Matching Method for Low Frequency GPS Data

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Zhijia Liu ◽  
Jie Fang ◽  
Mengyun Xu ◽  
Pinghui Xiao
Keyword(s):  
Low Frequency ◽  
Gps Data ◽  
Map Matching ◽  
Matching Method ◽  
Vehicle Trajectory

scholarly journals Deep learning enabled vehicle trajectory map-matching method with advanced spatial–temporal analysis

2020 ◽  
Vol 14 (14) ◽  
pp. 2052-2063
Author(s):  
Zhijia Liu ◽  
Jie Fang ◽  
Yingfang Tong ◽  
Mengyun Xu
Keyword(s):  
Deep Learning ◽  
Temporal Analysis ◽  
Map Matching ◽  
Matching Method ◽  
Vehicle Trajectory ◽  
Spatial Temporal Analysis

A Dynamic K-nearest Neighbor Map Matching Method Combined with Neural Network

2019 ◽  
Author(s):  
Ting Liu ◽  
Zhe Chen ◽  
Chuan Chen ◽  
Zongtao Duan ◽  
Bin Zhao
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Map Matching ◽  
K Nearest Neighbor ◽  
Matching Method

scholarly journals MAP MATCHING METHOD WITH CONSIDERATION OF ERROR IN NETWORK COORDINATES

2017 ◽  
Vol 73 (5) ◽  
pp. I_549-I_557
Author(s):  
Wataru NAKANISHI ◽  
Takashi FUSE
Keyword(s):  
Map Matching ◽  
Matching Method ◽  
Network Coordinates

scholarly journals Validation of Map Matching Algorithms using High Precision Positioning with GPS

2005 ◽  
Vol 58 (2) ◽  
pp. 257-271 ◽  
Author(s):  
Mohammed A. Quddus ◽  
Robert B. Noland ◽  
Washington Y. Ochieng
Keyword(s):  
High Precision ◽  
Dead Reckoning ◽  
Vehicle Speed ◽  
Map Matching ◽  
Precision Positioning ◽  
Digital Map ◽  
Mm Algorithm ◽  
Essential Components ◽  
Vehicle Trajectory ◽  
Map Data

Map Matching (MM) algorithms are usually employed for a range of transport telematics applications to correctly identify the physical location of a vehicle travelling on a road network. Two essential components for MM algorithms are (1) navigation sensors such as the Global Positioning System (GPS) and dead reckoning (DR), among others, to estimate the position of the vehicle, and (2) a digital base map for spatial referencing of the vehicle location. Previous research by the authors (Quddus et al., 2003; Ochieng et al., 2003) has developed improved MM algorithms that take account of the vehicle speed and the error sources associated with the navigation sensors and the digital map data previously ignored in conventional MM approaches. However, no validation study assessing the performance of MM algorithms has been presented in the literature. This paper describes a generic validation strategy and results for the MM algorithm previously developed in Ochieng et al. (2003). The validation technique is based on a higher accuracy reference (truth) of the vehicle trajectory as determined by high precision positioning achieved by the carrier-phase observable from GPS. The results show that the vehicle positions determined from the MM results are within 6 m of the true positions. The results also demonstrate the importance of the quality of the digital map data to the map matching process.

Research on a Map-matching Method for a GNSS-based ETC System

2012 ◽  
Vol 6 (4) ◽  
pp. 96-102
Author(s):  
Dongzhu Wang ◽  
Xin Geng ◽  
Shushan Zhu
Keyword(s):  
Map Matching ◽  
Matching Method

A Multiple-Hypothesis Map-Matching Method Suitable for Weighted and Box-Shaped State Estimation for Localization

2011 ◽  
Vol 12 (4) ◽  
pp. 1495-1510 ◽  
Author(s):  
Fahed Abdallah ◽  
Ghalia Nassreddine ◽  
Thierry Denoeux
Keyword(s):  
State Estimation ◽  
Map Matching ◽  
Matching Method ◽  
Multiple Hypothesis

scholarly journals A Map-matching Method Using a Chi-squared Statistic for Car-navigation Systems

2013 ◽  
Vol 2013 (0) ◽  
pp. 87-93 ◽  
Author(s):  
Toshiyuki AOKI ◽  
Mikio BANDO ◽  
Tomoaki HIRUTA ◽  
Koichi KATO ◽  
Akihiro KAWABATA ◽  
...  
Keyword(s):  
Navigation Systems ◽  
Map Matching ◽  
Matching Method ◽  
Chi Squared ◽  
Car Navigation
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