scholarly journals A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2057 ◽  
Author(s):  
Wentao Bian ◽  
Ge Cui ◽  
Xin Wang
Keyword(s):  
Sampling Rate ◽  
Longest Common Subsequence ◽  
Map Matching ◽  
Missing Information ◽  
The Road ◽  
Global Positioning ◽  
Common Subsequence ◽  
Gps Trajectories ◽  
Gps Trajectory ◽  
Effectiveness And Efficiency

GPS (Global Positioning System) trajectories with low sampling rates are prevalent in many applications. However, current map matching methods do not perform well for low-sampling-rate GPS trajectories due to the large uncertainty between consecutive GPS points. In this paper, a collaborative map matching method (CMM) is proposed for low-sampling-rate GPS trajectories. CMM processes GPS trajectories in batches. First, it groups similar GPS trajectories into clusters and then supplements the missing information by resampling. A collaborative GPS trajectory is then extracted for each cluster and matched to the road network, based on longest common subsequence (LCSS) distance. Experiments are conducted on a real GPS trajectory dataset and a simulated GPS trajectory dataset. The results show that the proposed CMM outperforms the baseline methods in both, effectiveness and efficiency.

scholarly journals An Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data

2019 ◽  
Vol 8 (9) ◽  
pp. 411 ◽  
Author(s):  
Tang ◽  
Deng ◽  
Huang ◽  
Liu ◽  
Chen
Keyword(s):  
Road Network ◽  
Sampling Rate ◽  
Quality Data ◽  
Automatic Method ◽  
Trajectory Data ◽  
The Road ◽  
High Sampling Rate ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently.

scholarly journals Development of a map-matching algorithm for dynamic-sampling-rate GPS signals to determine vehicle routes on a MATSim network

ORiON ◽  
2019 ◽  
Vol 35 (1) ◽  
pp. 1-31
Author(s):  
JB Vosloo ◽  
JW Joubert
Keyword(s):  
South Africa ◽  
Data Storage ◽  
Optimal Algorithm ◽  
Rapid Development ◽  
Sampling Rate ◽  
Cape Town ◽  
Map Matching ◽  
Matching Algorithm ◽  
Gps Trajectories ◽  
The City

The rapid development and proliferation of global positioning system (GPS)-enabled systems and devices have led to a significant increase in the availability of transport data, more specifically GPS trajectories, that can be used in researching vehicle activities. In order to save data storage- and handling costs many vehicle tracking systems only store low-frequency trajectories for vehicles. A number of existing methods used to map GPS trajectories to a digital road network were analysed and such an algorithm was implemented in Multi-Agent Transport Simulation (MATSim), an open source collaborative simulation package for Java. The map-matching algorithm was tested on a simple grid network and a real and extensive network of the City of Cape Town, South Africa. Experimentation showed the network size has the biggest influence on algorithm execution time and that a network must be reduced to include only the links that the vehicle most likely traversed. The algorithm is not suited for trajectories with sampling rates less than 5 seconds as it can result in unrealistic paths chosen, but it manages to obtain accuracies of around 80% up until sampling sizes of around 50 seconds whereafter the accuracy decreases. Further experimentation also revealed optimal algorithm parameters for matching trajectories on the Cape Town network. The use case for the implementation was to infer basic vehicle travel information, such as route travelled and speed of travel, for municipal waste collection vehicles in the City of Cape Town, South Africa.

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.

scholarly journals Extracting Flooded Roads by Fusing GPS Trajectories and Road Network

2019 ◽  
Vol 8 (9) ◽  
pp. 407
Author(s):  
She ◽  
Zhong ◽  
Fang ◽  
Zheng ◽  
Zhou
Keyword(s):  
Urban Areas ◽  
Urban Traffic ◽  
Rainfall Simulation ◽  
Map Matching ◽  
Negative Effects ◽  
High Position ◽  
Gps Trajectories ◽  
Gps Trajectory ◽  
Partition Statistics ◽  
Road Data

Urban roads are the lifeline of urban transportation and satisfy the commuting and travel needs of citizens. Following the acceleration of urbanization and the frequent extreme weather in recent years, urban waterlogging is occurring more than usual in summer and has negative effects on the urban traffic networks. Extracting flooded roads is a critical procedure for improving the resistance ability of roads after urban waterlogging occurs. This paper proposes a flooded road extraction method to extract the flooding degree and the time at which roads become flooded in large urban areas by using global positioning system (GPS) trajectory points with driving status information and the high position accuracy of vector road data with semantic information. This method uses partition statistics to create density grids (grid layer) and uses map matching to construct a time-series of GPS trajectory point density for each road (vector layer). Finally, the fusion of grids and vector layers obtains a more accurate result. The experiment uses a dataset of GPS trajectory points and vector road data in the Wuchang district, which proves that the extraction result has a high similarity with respect to the flooded roads reported in the news. Additionally, extracted flooded roads that were not reported in the news were also found. Compared with the traditional methods for extracting flooded roads and areas, such as rainfall simulation and SAR image-based classification in urban areas, the proposed method discovers hidden flooding information from geospatial big data, uploaded at no cost by urban taxis and remaining stable for a long period of time.

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