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...

Celý popis

Uloženo v:

Podrobná bibliografie
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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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
BookMark	eNptUV1rFDEUDVLB2vbNHxDwxQen5msyyWPdulqYWij2wadwJx9LlplkzcxS-u_NuiKlmDzkcnPOuZx73qKTlJNH6B0ll5xr8iluN5EySomQ3St0yhgjjdZSnDyr36CLed6SejTlSpBT9POhDJDwfQaHe0gef99Pgy_4NqaYNjiUPOE-Pzbr4n_tfbJPeD1mWA5_Kyj4GhbAn2H2DueEr73f4d5DOXDP0esA4-wv_r5n6GH95cfqW9Pffb1ZXfWNFVwvDTCphRvABUlDkEpxkF5TxQapB00pWAp0IL61lHEhiXTCQhharZwOWnF-hm6Oui7D1uxKnKA8mQzR_GnksjFQlmhHb7oh2LZjlAnRic61yjvNBtFqrkAQravWh6PWruRqd17MFGfrx7FuJu9nw4kgdW-athX6_gV0m_clVaeGKc2JJIqJiro8ojZQ58cU8lLA1uv8FG0NMMTav-o6wRmtnEr4eCTYkue5-PDPESXmkLN5nnOFsxdwG5caT051Thz_T_oNirypuA
CitedBy_id	crossref_primary_10_1109_JSTARS_2025_3548688 crossref_primary_10_3390_ijgi13030104
Cites_doi	10.3390/ijgi7080317 10.1109/TITS.2020.3040728 10.1109/5.726791 10.1007/978-3-319-11740-9_34 10.3390/ijgi4042660 10.1145/2424321.2424333 10.1016/j.trc.2018.02.007 10.1145/1869790.1869805 10.1007/s00138-011-0404-2 10.1016/j.trc.2021.103234 10.1109/TITS.2016.2521482 10.1109/TITS.2022.3222504 10.1080/13658816.2015.1092151 10.1080/13658816.2017.1402913 10.3390/su11164511 10.1109/ITSC.2013.6728262 10.1016/j.artint.2014.02.004
ContentType	Journal Article
Copyright	COPYRIGHT 2023 MDPI AG 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: COPYRIGHT 2023 MDPI AG – notice: 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION 7SC 7UA 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F1W FR3 H96 HCIFZ JQ2 KR7 L.G L6V L7M L~C L~D M7S P5Z P62 PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7S9 L.6 DOA
DOI	10.3390/ijgi12110467
DatabaseName	CrossRef Computer and Information Systems Abstracts Water Resources Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC ProQuest Central Technology Collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Central Korea ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources SciTech Premium Collection ProQuest Computer Science Collection Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Earth, Atmospheric & Aquatic Science Database Proquest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection AGRICOLA AGRICOLA - Academic DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Aquatic Science & Fisheries Abstracts (ASFA) Professional Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China Water Resources Abstracts Environmental Sciences and Pollution Management Earth, Atmospheric & Aquatic Science Collection ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection Natural Science Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Engineering Collection Advanced Technologies & Aerospace Collection Civil Engineering Abstracts Engineering Database ProQuest One Academic Eastern Edition Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources ProQuest One Academic UKI Edition ASFA: Aquatic Sciences and Fisheries Abstracts Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) AGRICOLA AGRICOLA - Academic
DatabaseTitleList	CrossRef Publicly Available Content Database AGRICOLA
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography Visual Arts
EISSN	2220-9964
ExternalDocumentID	oai_doaj_org_article_7bfc5721244747d58ed92b45938a4099 A774321060 10_3390_ijgi12110467
GeographicLocations	China
GeographicLocations_xml	– name: China
GroupedDBID	5VS 8FE 8FG 8FH AADQD AAFWJ AAHBH AAYXX ABJCF ADBBV ADMLS AENEX AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION GROUPED_DOAJ HCIFZ IAO ITC KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PHGZM PHGZT PIMPY PQGLB PROAC PTHSS ZBA 7SC 7UA 8FD ABUWG AZQEC C1K DWQXO F1W FR3 H96 JQ2 KR7 L.G L7M L~C L~D PKEHL PQEST PQQKQ PQUKI PRINS 7S9 L.6
ID	FETCH-LOGICAL-c439t-a2694dbadf61ff6883a6e9182b69b911ac1a1b0e5c1234606d4cafb598d9f9833
IEDL.DBID	DOA
ISICitedReferencesCount	3
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001118352100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2220-9964
IngestDate	Fri Oct 03 12:50:33 EDT 2025 Sun Nov 09 10:24:53 EST 2025 Fri Jul 25 09:34:43 EDT 2025 Tue Nov 04 18:32:22 EST 2025 Sat Nov 29 07:11:27 EST 2025 Tue Nov 18 19:49:09 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	11
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c439t-a2694dbadf61ff6883a6e9182b69b911ac1a1b0e5c1234606d4cafb598d9f9833
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0001-9022-6991
OpenAccessLink	https://doaj.org/article/7bfc5721244747d58ed92b45938a4099
PQID	2893060824
PQPubID	2032387
ParticipantIDs	doaj_primary_oai_doaj_org_article_7bfc5721244747d58ed92b45938a4099 proquest_miscellaneous_3040384915 proquest_journals_2893060824 gale_infotracacademiconefile_A774321060 crossref_primary_10_3390_ijgi12110467 crossref_citationtrail_10_3390_ijgi12110467
PublicationCentury	2000
PublicationDate	2023-11-01
PublicationDateYYYYMMDD	2023-11-01
PublicationDate_xml	– month: 11 year: 2023 text: 2023-11-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	ISPRS international journal of geo-information
PublicationYear	2023
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Tang (ref_12) 2017; 42 Zhao (ref_2) 2018; 1 ref_14 Arman (ref_15) 2021; 129 Lecun (ref_27) 1998; 86 ref_30 Li (ref_6) 2020; 6 Zhang (ref_26) 2020; 38 ref_19 Kuntzsch (ref_20) 2016; 30 Tang (ref_11) 2016; 17 Zhou (ref_18) 2023; 24 Tang (ref_10) 2016; 29 Qu (ref_25) 2019; 11 Tang (ref_9) 2015; 4 Yang (ref_3) 2016; 53 ref_24 Fan (ref_17) 2022; 33 ref_23 ref_22 ref_21 Baldi (ref_31) 2014; 210 ref_29 ref_28 Shu (ref_16) 2022; 23 Lerner (ref_1) 2014; 25 ref_8 Yang (ref_4) 2018; 89 ref_5 ref_7 Yang (ref_13) 2018; 32
References_xml	– volume: 1 start-page: 97 year: 2018 ident: ref_2 article-title: An Improved Map Matching Algorithm for Floating Car publication-title: Bull. Surv. Mapp. – ident: ref_28 – ident: ref_14 doi: 10.3390/ijgi7080317 – volume: 23 start-page: 4049 year: 2022 ident: ref_16 article-title: Efficient Lane-Level Map Building via Vehicle-Based Crowdsourcing publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2020.3040728 – ident: ref_30 – volume: 86 start-page: 2278 year: 1998 ident: ref_27 article-title: Gradient-based learning applied to document recognition publication-title: Proc. IEEE doi: 10.1109/5.726791 – ident: ref_29 doi: 10.1007/978-3-319-11740-9_34 – ident: ref_24 – volume: 4 start-page: 2660 year: 2015 ident: ref_9 article-title: Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification publication-title: ISPRS Int. J. Geo-Inf. doi: 10.3390/ijgi4042660 – ident: ref_19 doi: 10.1145/2424321.2424333 – volume: 89 start-page: 168 year: 2018 ident: ref_4 article-title: Generating lane-based intersection maps from crowdsourcing big trace data publication-title: Transp. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2018.02.007 – volume: 29 start-page: 116 year: 2016 ident: ref_10 article-title: Traffic Line Numbers Detection Based on the Naïve Bayesian Classification publication-title: China J. Highw. Transp. – volume: 11 start-page: 89 year: 2019 ident: ref_25 article-title: Application of DBSCAN Clustering and Improved Bilateral Filtering Algorithm in Point Cloud Denoising publication-title: Bull. Surv. Mapp. – volume: 6 start-page: 22 year: 2020 ident: ref_6 article-title: Summary of road information extraction methods publication-title: Bull. Surv. Mapp. – ident: ref_8 doi: 10.1145/1869790.1869805 – ident: ref_23 – volume: 25 start-page: 727 year: 2014 ident: ref_1 article-title: Recent progress in road and lane detection: A survey publication-title: Mach. Vis. Appl. doi: 10.1007/s00138-011-0404-2 – volume: 33 start-page: 6040122 year: 2022 ident: ref_17 article-title: Lane-Level Road Map Construction considering Vehicle Lane-Changing Behavior publication-title: J. Adv. Transp. – ident: ref_21 – volume: 129 start-page: 103234 year: 2021 ident: ref_15 article-title: Lane-level routable digital map reconstruction for motorway networks using low-precision GPS data publication-title: Transp. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2021.103234 – volume: 17 start-page: 2552 year: 2016 ident: ref_11 article-title: CLRIC: Collecting Lane-Based Road Information Via Crowdsourcing publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2016.2521482 – volume: 42 start-page: 341 year: 2017 ident: ref_12 article-title: Traffic Lane Number Extraction Based on the Constrained Gaussian Mixture Model publication-title: Geomat. Inf. Sci. Wuhan Univ. – volume: 24 start-page: 7780 year: 2023 ident: ref_18 article-title: Lane Information Extraction for High Definition Maps Using Crowdsourced Data publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2022.3222504 – volume: 30 start-page: 1012 year: 2016 ident: ref_20 article-title: Generative models for road network reconstruction publication-title: Int. J. Geogr. Inf. Sci. doi: 10.1080/13658816.2015.1092151 – volume: 38 start-page: 293 year: 2020 ident: ref_26 article-title: Floating Car Data Preprocessing Based on DBSCAN Algorithm publication-title: Jiangxi Sci. – volume: 32 start-page: 601 year: 2018 ident: ref_13 article-title: Automatic change detection in lane-level road networks using GPS trajectories publication-title: Int. J. Geogr. Inf. Sci. doi: 10.1080/13658816.2017.1402913 – ident: ref_22 – ident: ref_5 doi: 10.3390/su11164511 – ident: ref_7 doi: 10.1109/ITSC.2013.6728262 – volume: 210 start-page: 78 year: 2014 ident: ref_31 article-title: The dropout learning algorithm publication-title: Artif. Intell. doi: 10.1016/j.artint.2014.02.004 – volume: 53 start-page: 2681 year: 2016 ident: ref_3 article-title: A Method for Road Network Updating Based on Vehicle Trajectory Big Data publication-title: J. Comput. Res. Dev.
SSID	ssj0000913840
Score	2.292671
Snippet	Lane-level road information is especially crucial now that high-precision navigation maps are in more demand. Road information may be obtained rapidly and...
SourceID	doaj proquest gale crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
StartPage	467
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
SummonAdditionalLinks	– databaseName: Engineering Database dbid: M7S link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BQaI9FChFhBZkJBAHFDVPr31CfbDiUFYIKCona2I7y1ZVsiS7Rf33jB3vlku5cE0sy8mM52HPfB_Aa65znhfCxlyiiclDZTGSG411hqmwWKNN0JNNjCYTcX4uP4cDtz6UVa5sojfUptXujPyAEgOKbslhFe_nv2LHGuVuVwOFxl2451ASUl-693V9xuIwLymBGerdc8ruD2YX05kDNUsGYvkbT-QB-28zy97XjB_-7yofwXaIMtnhoBaP4Y5tduBBIDz_eb0DW99n_XIY0T-BH2ddhQ370qJhp9hYNvE8IeyTZ49grgWFnba_43E3FF5fs_Fli65gmh1jx05wgeyI3KFhbcNOrJ2zANs63YWz8Ydvxx_jwLkQawpNFiQoLgtToal5WtdciBy5lZSEVFxWZBhRp5hWiS01ubyCsh9TaKyrUgojayny_ClsNG1jnwGTwoOVlVZyW2BmREU_vqa5RxkWST2K4N3q_ysdAMkdL8alosTESUv9La0I3qxHzwcgjlvGHTlRrsc4-Gz_oO2mKuxGNapqXVLuS7ENpVOmFNbIrCpKmQtampQRvHWKoNwmpyVpDL0K9GEOLksdUtDsmp94EsH-ShFU2P29utGCCF6tX9O-dZcxJMV22aucrCcpp0zL5_-eYg82HcX90P-4DxuLbmlfwH19tZj13Uuv8H8ATdgHUg priority: 102 providerName: ProQuest
Title	Urban Road Lane Number Mining from Low-Frequency Floating Car Data Based on Deep Learning
URI	https://www.proquest.com/docview/2893060824 https://www.proquest.com/docview/3040384915 https://doaj.org/article/7bfc5721244747d58ed92b45938a4099
Volume	12
WOSCitedRecordID	wos001118352100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: DOA dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: P5Z dateStart: 20120301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: PCBAR dateStart: 20120301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: M7S dateStart: 20120301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: BENPR dateStart: 20120301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: PIMPY dateStart: 20120301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB2hggQcEBQQacvKSCAOKOrmy7GP3XZXILWrqFDUcrEmtlMWVUmV7IJ64bczdtJlLxUXLjkkVuSMx555ysx7AG-5TniSChtyiSakCBWHSGE01DFGwmKFdoxebCKfz8X5uSw2pL5cTVhPD9wbbj8vK50RTKEwRJmvyYQ1Mi7TTCYCCZv41r1xLjfAlD-DZZQQdOkr3RPC9fuLH5cLR2c27iXl_8YgT9V_14Hso8zsKTwZ0kN20E_rGdyz9TY8HJTKv99sw-Ovi27Vj-iew8VZW2LNThs07Bhry-Ze4IOdeNkH5npH2HHzK5y1fcX0DZtdNegqndkhtuwIl8gmFMcMa2p2ZO01G_hWL1_A2Wz65fBjOIglhJpyiiVZmMvUlGgqHlUVFyJBbiWhh5LLkk401BFG5dhmmmJVSrDFpBqrMpPCyEqKJHkJW3VT21fApPAsY5mV3KYYG1GS3Sp6dx6Tyas8gA-35lN6YBJ3ghZXihCFM7baNHYA79ajr3sGjTvGTdxKrMc43mt_g7xBDd6g_uUNAbx366jc7qQpaRyaDOjDHM-VOqBs13Ut8XEAe7dLrYZt2ylCnwShKCtKA3izfkwbzv1FoVVsVp1K6Ngj35JRtvM_ZrwLj5yCfd_euAdby3ZlX8MD_XO56NoR3J9M58XpyHv3yBWmfnbX31O6Ftk3el58Oiku_gAobv5n
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1NbxMxEB1VKVLhwEcBkVLASFQc0Kr7HfuAUNsQNWoSRahF7cmdtb0hVbUbdhOq_Cl-I-PdTcql3HrgumtZ3vXzvBnbMw_gQ6yCOAi5cWKB2iGG8h0kGnWUjx43mKJxsRKb6IxG_PxcjDfg9yoXxl6rXNnEylDrXNk98n0KDMi7JcIKv8x-OlY1yp6uriQ0alicmOUNhWzl536X5nfP93tfT4-OnUZVwFFEvnMaSixCnaBOYy9NY84DjI0gNzuJRUJLH5WHXuKaSJFRD8m_16HCNIkE1yIV3G6AksnfDC3YW7A57g_HF-tdHVtlk0Km-oZ9EAh3f3o1mdoyam4tZX_LfZVEwF1EULFb78n_9l-ewuPGj2YHNfCfwYbJtmGrkXT_sdyGR9-n5aJuUT6Hi7MiwYx9y1GzAWaGjSolFDas9DGYTbJhg_zG6RX11fIl613naK-EsyMsWBfnyA6J8DXLM9Y1ZsaawrSTF3B2L9_5ElpZnplXwASvyrFFRsQmRF_zhCY6pb47PoZu2mnDp9V8S9WUXLfKH9eSQi-LDvk3Otqwt249q0uN3NHu0EJn3cYWCK8e5MVENvZGdpJURRTdk_dGAaOOuNHCT8JIBJyGJkQbPlrgSWvGaEgKm2wM-jBbEEweUFhg07titw27K-DJxr6V8hZ1bXi_fk2WyR430Szmi1IGxA-0GIQX7fy7i3ewdXw6HMhBf3TyGh765EbW2Z670JoXC_MGHqhf82lZvG2WG4PL-0byH0v6ZiI
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9NAEB1VBUE58FFAGAosEhUHZMXf3j0g1DZEVA1RhChquSzj3XUaVNnBTqjy1_h1zNpOyqXceuBqr6y19-2bmfXMPIDXiQqTMOLGTQRqlyxU4CKZUVcF6HODORoPG7GJdDTiJydivAG_V7UwNq1yxYkNUetS2TPyHgUG5N2SwYp6eZcWMe4P3s9-ulZByv5pXclptBA5MssLCt_qd4d9WuvdIBh8-HLw0e0UBlxFhnhO00pEpDPUeeLnecJ5iIkR5HJniciIBlD56GeeiRURfES-vo4U5lksuBa54PYwlOj_Rkoxpk0nHMff1uc7tt8mBU9trn0YCq83_TGZ2oZqXitqf2kFG7GAq0xCY-cG9_7nL3Qf7nbeNdtrt8MD2DDFNtzuhN7Plttw5-u0XrQj6odwelxlWLDPJWo2xMKwUaOPwj41qhnMlt6wYXnhDqo24XzJBucl2kRxdoAV6-Mc2T65AZqVBesbM2Ndu9rJIzi-lvd8DJtFWZgnwARvmrTFRiQmwkDzjBY9p2enAUZenjrwdrX2UnWN2K0eyLmkgMwiRf6NFAd216NnbQOSK8btWxitx9i24c2FsprIjoVkmuUqppiffDoKI3XMjRZBFsUi5DQ1IRx4Y0EoLbnRlBR2NRr0YrZNmNyjYMEWfSWeAzsrEMqO9Wp5iUAHXq1vE1_Zn1C0iuWiliFZDdoYwo-f_vsRL-EWwVcOD0dHz2ArIN-yLQHdgc15tTDP4ab6NZ_W1Ytm3zH4ft0w_gN3zm2F
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Urban+Road+Lane+Number+Mining+from+Low-Frequency+Floating+Car+Data+Based+on+Deep+Learning&rft.jtitle=ISPRS+international+journal+of+geo-information&rft.au=Li%2C+Xiaolong&rft.au=Zhang%2C+Yun&rft.au=Xiang%2C+Longgang&rft.au=Wu%2C+Tao&rft.date=2023-11-01&rft.issn=2220-9964&rft.eissn=2220-9964&rft.volume=12&rft.issue=11&rft.spage=467&rft_id=info:doi/10.3390%2Fijgi12110467&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_ijgi12110467
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2220-9964&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2220-9964&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2220-9964&client=summon