Development of map matching algorithm for low frequency probe data

2012 ◽  
Vol 22 ◽  
pp. 132-145 ◽  
Author(s):  
Tomio Miwa ◽  
Daisuke Kiuchi ◽  
Toshiyuki Yamamoto ◽  
Takayuki Morikawa
Keyword(s):  
Low Frequency ◽  
Map Matching ◽  
Matching Algorithm

Map-matching algorithm for large-scale low-frequency floating car data

2013 ◽  
Vol 28 (1) ◽  
pp. 22-38 ◽  
Author(s):  
Bi Yu Chen ◽  
Hui Yuan ◽  
Qingquan Li ◽  
William H.K. Lam ◽  
Shih-Lung Shaw ◽  
...  
Keyword(s):  
Large Scale ◽  
Low Frequency ◽  
Map Matching ◽  
Matching Algorithm ◽  
Floating Car Data

scholarly journals A Data Correction Algorithm for Low-Frequency Floating Car Data

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3639 ◽  
Author(s):  
Bijun Li ◽  
Yuan Guo ◽  
Jian Zhou ◽  
Yi Cai
Keyword(s):  
Low Cost ◽  
Low Frequency ◽  
Map Matching ◽  
Correction Algorithm ◽  
Trajectory Data ◽  
Matching Algorithm ◽  
Data Correction ◽  
Floating Car Data ◽  
Attraction Model ◽  
Physical Attraction

The data collected by floating cars is an important source for lane-level map production. Compared with other data sources, this method is a low-cost but challenging way to generate high-accuracy maps. In this paper, we propose a data correction algorithm for low-frequency floating car data. First, we preprocess the trajectory data by an adaptive density optimizing method to remove the noise points with large mistakes. Then, we match the trajectory data with OpenStreetMap (OSM) using an efficient hierarchical map matching algorithm. Lastly, we correct the floating car data by an OSM-based physical attraction model. Experiments are conducted exploiting the data collected by thousands of taxies over one week in Wuhan City, China. The results show that the accuracy of the data is improved and the proposed algorithm is demonstrated to be practical and effective.

scholarly journals Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

2021 ◽  
Vol 10 (2) ◽  
pp. 79
Author(s):  
Ching-Yun Mu ◽  
Tien-Yin Chou ◽  
Thanh Van Hoang ◽  
Pin Kung ◽  
Yao-Min Fang ◽  
...  
Keyword(s):  
Data Structures ◽  
Spatial Data ◽  
Spatial Information ◽  
Data Retrieval ◽  
Spatial Data Mining ◽  
Map Matching ◽  
Matching Algorithm ◽  
Table Method ◽  
Look Up Table ◽  
The Look

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

A turning point-based offline map matching algorithm for urban road networks

2021 ◽  
Vol 565 ◽  
pp. 32-45
Author(s):  
Dongqing Zhang ◽  
Yucheng Dong ◽  
Zhaoxia Guo
Keyword(s):  
Turning Point ◽  
Road Networks ◽  
Map Matching ◽  
Matching Algorithm ◽  
Urban Road

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
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