{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T06:32:49Z","timestamp":1784269969209,"version":"3.55.0"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundations of China","award":["42261078"],"award-info":[{"award-number":["42261078"]}]},{"name":"National Natural Science Foundations of China","award":["20223BBE51030"],"award-info":[{"award-number":["20223BBE51030"]}]},{"name":"National Natural Science Foundations of China","award":["2022JXDZKJKY08"],"award-info":[{"award-number":["2022JXDZKJKY08"]}]},{"name":"Jiangxi Provincial Key R&amp;D Program","award":["42261078"],"award-info":[{"award-number":["42261078"]}]},{"name":"Jiangxi Provincial Key R&amp;D Program","award":["20223BBE51030"],"award-info":[{"award-number":["20223BBE51030"]}]},{"name":"Jiangxi Provincial Key R&amp;D Program","award":["2022JXDZKJKY08"],"award-info":[{"award-number":["2022JXDZKJKY08"]}]},{"name":"Science and Technology Research Project of the Jiangxi Bureau of Geology","award":["42261078"],"award-info":[{"award-number":["42261078"]}]},{"name":"Science and Technology Research Project of the Jiangxi Bureau of Geology","award":["20223BBE51030"],"award-info":[{"award-number":["20223BBE51030"]}]},{"name":"Science and Technology Research Project of the Jiangxi Bureau of Geology","award":["2022JXDZKJKY08"],"award-info":[{"award-number":["2022JXDZKJKY08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/ijgi12110467","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:30:50Z","timestamp":1700440250000},"page":"467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Urban Road Lane Number Mining from Low-Frequency Floating Car Data Based on Deep Learning"],"prefix":"10.3390","volume":"12","author":[{"given":"Xiaolong","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China"},{"name":"CNNC Engineering Research Center of 3D Geographic Information, East China University of Technology, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Railway Water Resources and Hydropower Planning and Design Group, Nanchang 330001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9022-6991","authenticated-orcid":false,"given":"Longgang","family":"Xiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Hunan Normal University, Changsha 410012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s00138-011-0404-2","article-title":"Recent progress in road and lane detection: A survey","volume":"25","author":"Lerner","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_2","first-page":"97","article-title":"An Improved Map Matching Algorithm for Floating Car","volume":"1","author":"Zhao","year":"2018","journal-title":"Bull. Surv. Mapp."},{"key":"ref_3","first-page":"2681","article-title":"A Method for Road Network Updating Based on Vehicle Trajectory Big Data","volume":"53","author":"Yang","year":"2016","journal-title":"J. Comput. Res. Dev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.trc.2018.02.007","article-title":"Generating lane-based intersection maps from crowdsourcing big trace data","volume":"89","author":"Yang","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zheng, L., Li, B., Yang, B., Song, H., and Lu, Z. (2019). Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey. Sustainability, 11.","DOI":"10.3390\/su11164511"},{"key":"ref_6","first-page":"22","article-title":"Summary of road information extraction methods","volume":"6","author":"Li","year":"2020","journal-title":"Bull. Surv. Mapp."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Uduwaragoda, E.R.I.A.C.M., Perera, A.S., and Dias, S.A.D. (2013, January 6\u20139). Generating lane level road data from vehicle trajectories using kernel density estimation. Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, The Netherlands.","DOI":"10.1109\/ITSC.2013.6728262"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Krumm, J. (2010, January 3\u20135). Probabilistic Modeling of Traffic Lanes from GPS Traces. Proceedings of the 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM-GIS2010), San Jose, CA, USA.","DOI":"10.1145\/1869790.1869805"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2660","DOI":"10.3390\/ijgi4042660","article-title":"Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Na\u00efve Bayesian Classification","volume":"4","author":"Tang","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_10","first-page":"116","article-title":"Traffic Line Numbers Detection Based on the Na\u00efve Bayesian Classification","volume":"29","author":"Tang","year":"2016","journal-title":"China J. Highw. Transp."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2552","DOI":"10.1109\/TITS.2016.2521482","article-title":"CLRIC: Collecting Lane-Based Road Information Via Crowdsourcing","volume":"17","author":"Tang","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","first-page":"341","article-title":"Traffic Lane Number Extraction Based on the Constrained Gaussian Mixture Model","volume":"42","author":"Tang","year":"2017","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1080\/13658816.2017.1402913","article-title":"Automatic change detection in lane-level road networks using GPS trajectories","volume":"32","author":"Yang","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, Y., Tan, Y., Cheng, P., Wu, J., and Wang, Y. (2018). Method Based on Floating Car Data and Gradient Boosted Decision Tree Classification for the Detection of Auxiliary Through Lanes at Intersections. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7080317"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103234","DOI":"10.1016\/j.trc.2021.103234","article-title":"Lane-level routable digital map reconstruction for motorway networks using low-precision GPS data","volume":"129","author":"Arman","year":"2021","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1109\/TITS.2020.3040728","article-title":"Efficient Lane-Level Map Building via Vehicle-Based Crowdsourcing","volume":"23","author":"Shu","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","first-page":"6040122","article-title":"Lane-Level Road Map Construction considering Vehicle Lane-Changing Behavior","volume":"33","author":"Fan","year":"2022","journal-title":"J. Adv. Transp."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7780","DOI":"10.1109\/TITS.2022.3222504","article-title":"Lane Information Extraction for High Definition Maps Using Crowdsourced Data","volume":"24","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Biagioni, J., and Eriksson, J. (2012, January 6\u20139). Map inference in the face of noise and disparity. Proceedings of the 20th International Conference on Advances in Geographic Information Systems, Redondo Beach, CA, USA.","DOI":"10.1145\/2424321.2424333"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1080\/13658816.2015.1092151","article-title":"Generative models for road network reconstruction","volume":"30","author":"Kuntzsch","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_21","unstructured":"Chen, L. (2018). Research on Information Mining of Taxi GPS Data. [Master\u2019s Thesis, Beijing Jiaotong University]."},{"key":"ref_22","unstructured":"Lv, Z. (2016). Research on GPS Data Preprocessing of Floating Car in Urban Traffic Guidance System. [Master\u2019s Thesis, Lanzhou Jiaotong University]."},{"key":"ref_23","unstructured":"Ester, M., Kr\u00f6ger, P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial database. Proceedings of the 2th International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA."},{"key":"ref_24","unstructured":"Guo, M. (2018). Research about DBSCAN Next Clustering Based on Spark Platform. [Master\u2019s Thesis, Beijing University of Technology]."},{"key":"ref_25","first-page":"89","article-title":"Application of DBSCAN Clustering and Improved Bilateral Filtering Algorithm in Point Cloud Denoising","volume":"11","author":"Qu","year":"2019","journal-title":"Bull. Surv. Mapp."},{"key":"ref_26","first-page":"293","article-title":"Floating Car Data Preprocessing Based on DBSCAN Algorithm","volume":"38","author":"Zhang","year":"2020","journal-title":"Jiangxi Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_28","unstructured":"Qin, Y. (2019). Research on Key Technologies of Traffic Sign Detection and Recognition. [Master\u2019s Thesis, Changchun University of Science and Technology]."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yu, D., Wang, H., Chen, P., and Wei, Z. (2014, January 24\u201326). Mixed Pooling for Convolutional Neural Networks. Proceedings of the 9th International Conference of Rough Sets and Knowledge Technology, Shanghai, China.","DOI":"10.1007\/978-3-319-11740-9_34"},{"key":"ref_30","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., and Sutskever, I. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv Prepr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.artint.2014.02.004","article-title":"The dropout learning algorithm","volume":"210","author":"Baldi","year":"2014","journal-title":"Artif. Intell."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/11\/467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:25:30Z","timestamp":1760131530000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/11\/467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":31,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["ijgi12110467"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12110467","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,18]]}}}