Urban Road Lane Number Mining from Low-Frequency Floating Car Data Based on Deep Learning

Lane-level road information is especially crucial now that high-precision navigation maps are in more demand. Road information may be obtained rapidly and affordably by mining floating vehicle data (FCD). A method is proposed to extract the number of lanes on urban roads by combining deep learning a...

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Vydáno v:ISPRS international journal of geo-information Ročník 12; číslo 11; s. 467
Hlavní autoři: Li, Xiaolong, Zhang, Yun, Xiang, Longgang, Wu, Tao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.11.2023
Témata:
Accuracy
Algorithms
Classification
Cleaning
Clustering
Computational linguistics
DBSCAN clustering algorithm
Deep learning
extracts
FCD
Floating
Fuzzy logic
Language processing
LeNet-5 model
mining
Natural language interfaces
Navigation
number of lanes
prediction
roads
Roads & highways
sampling
Urban areas
China
ISSN:2220-9964, 2220-9964
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Shrnutí:Lane-level road information is especially crucial now that high-precision navigation maps are in more demand. Road information may be obtained rapidly and affordably by mining floating vehicle data (FCD). A method is proposed to extract the number of lanes on urban roads by combining deep learning and low-frequency FCD. Initially, the FCD is cleaned using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering technique. Then, the FCD is split into three categories based on the typical urban road types: one-way one-lane, one-way two-lane, and one-way three-lane, and the deep learning sample data is created using segmentation, rotation, and gridding. Lastly, the number of urban road lanes is obtained by training and predicting the sample data using the LeNet-5 model. The number of urban road lanes was effectively identified from the low-frequency FCD with a detection accuracy of 92.7% through the cleaning and classification of Wuhan FCD. Urban roads can be efficiently covered by the FCD on a regular basis, and lane information can be efficiently collected using deep learning techniques. This method can be used to generate and update lane number information for high-precision navigation maps.
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ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi12110467