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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Published in:	ISPRS international journal of geo-information Vol. 12; no. 11; p. 467
Main Authors:	Li, Xiaolong, Zhang, Yun, Xiang, Longgang, Wu, Tao
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.11.2023
Subjects:	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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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Xiang, Longgang Wu, Tao Li, Xiaolong Zhang, Yun
Author_xml	– sequence: 1 givenname: Xiaolong surname: Li fullname: Li, Xiaolong – sequence: 2 givenname: Yun surname: Zhang fullname: Zhang, Yun – sequence: 3 givenname: Longgang orcidid: 0000-0001-9022-6991 surname: Xiang fullname: Xiang, Longgang – sequence: 4 givenname: Tao surname: Wu fullname: Wu, Tao
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SubjectTerms	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
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Title	Urban Road Lane Number Mining from Low-Frequency Floating Car Data Based on Deep Learning
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