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.

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

A trajectory map matching algorithm via exploring the average directional features of continuous windows

2021 ◽  
pp. 1-16
Author(s):  
Xiaohan Wang ◽  
Pei Wang ◽  
Weilong Chen ◽  
Wangwu Hu ◽  
Long Yang
Keyword(s):  
Road Network ◽  
Transition Probability ◽  
Location Based Services ◽  
Original Position ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Accuracy ◽  
Data Set ◽  
Matching Algorithm ◽  
Low Efficiency

Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective.

scholarly journals Local Path Searching Based Map Matching Algorithm for Floating Car Data

2011 ◽  
Vol 10 ◽  
pp. 576-582 ◽  
Author(s):  
Feng Chen ◽  
Mingyu Shen ◽  
Yongning Tang
Keyword(s):  
Map Matching ◽  
Matching Algorithm ◽  
Floating Car Data ◽  
Local Path

scholarly journals Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data

2017 ◽  
Vol 2645 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Lei Zhu ◽  
Jacob R. Holden ◽  
Jeffrey D. Gonder
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Longest Common Subsequence ◽  
Gps Data ◽  
Map Matching ◽  
Trajectory Data ◽  
Data Set ◽  
Matching Algorithm ◽  
Gps Trajectory ◽  
Trajectory Segmentation

With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similarity score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.

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

scholarly journals LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5815
Author(s):  
Nico Podevijn ◽  
Jens Trogh ◽  
Michiel Aernouts ◽  
Rafael Berkvens ◽  
Luc Martens ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Energy Efficient ◽  
Low Cost ◽  
Map Matching ◽  
Directional Information ◽  
Matching Algorithm ◽  
Additional Information ◽  
Energy Impact ◽  
Node Location ◽  
The Cost

In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is performed by the Time Difference of Arrival (TDoA) method and provides discrete position estimates on a map. An accurate “tracking-on-demand” mode for retrieving lost and stolen assets is important. To enable this mode, we propose deploying an e-compass in the mobile LoRa node, which frequently communicates directional information via the payload of the LoRaWAN uplink messages. Fusing this additional information with raw TDoA estimates in a map matching algorithm enables us to estimate the node location with a much increased accuracy. It is shown that this sensor fusion technique outperforms raw TDoA at the cost of only embedding a low-cost e-compass. For driving, cycling, and walking trajectories, we obtained minimal improvements of 65, 76, and 82% on the median errors which were reduced from 206 to 68 m, 197 to 47 m, and 175 to 31 m, respectively. The energy impact of adding an e-compass is limited: energy consumption increases by only 10% compared to traditional LoRa localization, resulting in a solution that is still 14 times more energy-efficient than a GPS-over-LoRa solution.

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