{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:47:43Z","timestamp":1780919263233,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T00:00:00Z","timestamp":1585094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["41971421 and 41601495"],"award-info":[{"award-number":["41971421 and 41601495"]}]},{"name":"Research Project of Education Department of Hunan Province","award":["18C0228"],"award-info":[{"award-number":["18C0228"]}]},{"name":"Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing] grant number","award":["17S01"],"award-info":[{"award-number":["17S01"]}]},{"name":"Open Fund of Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province (Changsha University of Science &amp; Technology)","award":["kfj170605, kfj150603, and kfj180604"],"award-info":[{"award-number":["kfj170605, kfj150603, and kfj180604"]}]},{"name":"Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems (Changsha University of Science &amp; Technology)] grant number","award":["kfj190703"],"award-info":[{"award-number":["kfj190703"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>With the rapid development of urban traffic, accurate and up-to-date road maps are in crucial demand for daily human life and urban traffic control. Recently, with the emergence of crowdsourced mapping, a surge in academic attention has been paid to generating road networks from spatio-temporal trajectory data. However, most existing methods do not explore changing road patterns contained in multi-temporal trajectory data and it is still difficult to satisfy the precision and efficiency demands of road information extraction. Hence, in this paper, we propose a hybrid method to incrementally extract urban road networks from spatio-temporal trajectory data. First, raw trajectory data were partitioned into K time slices and were used to initialize K-temporal road networks by a mathematical morphology method. Then, the K-temporal road networks were adjusted according to a gravitation force model so as to amend their geometric inconsistencies. Finally, road networks were geometrically delineated using the k-segment fitting algorithm, and the associated road attributes (e.g., road width and driving rule) were inferred. Several case studies were examined to demonstrate that our method can effectively improve the efficiency and precision of road extraction and can make a significant attempt to mine the incremental change patterns in road networks from spatio-temporal trajectory data to help with road map renewal.<\/jats:p>","DOI":"10.3390\/ijgi9040186","type":"journal-article","created":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T13:10:47Z","timestamp":1585141847000},"page":"186","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Hybrid Method to Incrementally Extract Road Networks Using Spatio-Temporal Trajectory Data"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0227-4940","authenticated-orcid":false,"given":"Yunfei","family":"Zhang","sequence":"first","affiliation":[{"name":"Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science &amp; Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zexu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science &amp; Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7163-6749","authenticated-orcid":false,"given":"Jincai","family":"Huang","sequence":"additional","affiliation":[{"name":"Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science &amp; Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"She","sequence":"additional","affiliation":[{"name":"Department of Geo-Informatics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Geo-Informatics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongchao","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim 7491, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Xu","sequence":"additional","affiliation":[{"name":"Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science &amp; Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingshen","family":"Deng","sequence":"additional","affiliation":[{"name":"Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science &amp; Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.3390\/ijgi4031246","article-title":"A Progressive Buffering Method for Road Map Update Using OpenStreetMap Data","volume":"4","author":"Liu","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zheng, L., Song, H., Li, B., and Zhang, H. (2019). Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8090416"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1111\/tgis.12186","article-title":"Autom. Update of Road Attributes by Mining GPS Tracks","volume":"20","author":"Biljecki","year":"2016","journal-title":"Trans. Gis"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1080\/13658816.2018.1510124","article-title":"Generating urban road intersection models from low-frequency GPS trajectory data","volume":"32","author":"Deng","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, W., Ai, T., and Lu, W. (2018). A Method for Extracting Road Boundary Information from Crowdsourcing Vehicle GPS Trajectories. Sensors, 18.","DOI":"10.3390\/s18041261"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"61","DOI":"10.3141\/2291-08","article-title":"Inferring Road Maps from Global Positioning System Traces","volume":"2291","author":"Biagioni","year":"2012","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_7","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":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cao, L., and Krumm, J. (2009, January 4\u20136). From GPS traces to a routable road map. Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, Washington, DC, USA.","DOI":"10.1145\/1653771.1653776"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tang, L., Ren, C., Liu, Z., and Li, Q. (2017). A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6020045"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.isprsjprs.2017.11.014","article-title":"Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images","volume":"138","author":"Maboudi","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2017.02.014","article-title":"Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data","volume":"126","author":"Yang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s10707-014-0222-6","article-title":"A comparison and evaluation of map construction algorithms using vehicle tracking data","volume":"19","author":"Ahmed","year":"2014","journal-title":"GeoInformatica"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"12752","DOI":"10.1073\/pnas.1821667116","article-title":"Quantifying the sensing power of vehicle fleets","volume":"116","author":"Anjomshoaa","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.trc.2012.09.007","article-title":"A pedestrian network construction algorithm based on multiple GPS traces","volume":"26","author":"Kasemsuppakorn","year":"2013","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1080\/15230406.2016.1190300","article-title":"Deriving incline values for street networks from voluntarily collected GPS traces","volume":"44","author":"John","year":"2016","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, C., and Chiang, M. (2016, January 25\u201327). Trajectory pattern mining: Exploring semantic and time information. Proceedings of the 2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2016), Hsinchu, Taiwan.","DOI":"10.1109\/TAAI.2016.7880171"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.trc.2017.11.021","article-title":"Inferring transportation modes from GPS trajectories using a convolutional neural network","volume":"86","author":"Dabiri","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1080\/15230406.2015.1130649","article-title":"Travel Time Estimation at Intersections Based on Low-frequency Spatial-temporal GPS Trace Big Data","volume":"43","author":"Tang","year":"2016","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ivanovic, S.S., Olteanu-Raimond, A.M., Musti\u00e8re, S., and Devogele, T. (2019). A Filtering-Based Approach for Improving Crowdsourced GNSS Traces in a Data Update Context. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8090380"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zang, K., Shen, J., Huang, H., Wan, M., and Shi, J. (2018). Assessing and mapping of road surface roughness based on GPS and accelerometer sensors on bicycle-mounted smartphones. Sensors, 18.","DOI":"10.3390\/s18030914"},{"key":"ref_22","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 International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, The Netherlands.","DOI":"10.1109\/ITSC.2013.6728262"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shi, W., Shen, S., and Liu, Y. (2009, January 4\u20137). Automatic Generation of Road Network Map from Massive GPS Vehicle Trajectories. Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA.","DOI":"10.1109\/ITSC.2009.5309871"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, L., Thiemann, F., and Sester, M. (2010). Integration of GPS traces with road map. International Workshop on Computational Transportation Science, ACM.","DOI":"10.1145\/1899441.1899447"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1109\/TITS.2010.2069097","article-title":"Robust Inference of Principal Road Paths for Intelligent Transportation Systems","volume":"12","author":"Agamennoni","year":"2011","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1023\/B:DAMI.0000026904.74892.89","article-title":"Mining GPS Traces for Map Refinement","volume":"9","author":"Schroedl","year":"2004","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/MPRV.2006.83","article-title":"Scalable, distributed, real-time map generation","volume":"5","author":"Davies","year":"2006","journal-title":"IEEE Pervasive Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.compenvurbsys.2016.12.006","article-title":"Automatic intersection and traffic rule detection by mining motor-vehicle GPS trajectories","volume":"64","author":"Wang","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Wu, H., Xu, Z., and Wu, G. (2019). A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8030142"},{"key":"ref_31","unstructured":"Bruntrup, R., Edelkamp, S., Jabbar, S., and Scholz, B. (2005, January 16). Incremental map generation with GPS traces. Proceedings of the Intelligent Transportation Systems, Vienna, Austria."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tang, J., Deng, M., Huang, J., Liu, H., and Chen, X. (2019). An Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8090411"},{"key":"ref_33","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":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ahmed, M., and Wenk, C. (2012, January 10\u201312). Constructing street networks from GPS trajectories. Proceedings of the European Symposium on Algorithms, Berlin, Germany.","DOI":"10.1007\/978-3-642-33090-2_7"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wu, T., Xiang, L., and Gong, J. (2016). Updating Road Networks by Local Renewal from GPS Trajectories. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5090163"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/13658816.2014.944527","article-title":"A novel approach for generating routable road maps from vehicle GPS traces","volume":"29","author":"Wang","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, X., Tang, L., Ren, C., Chen, Y., Xie, Z., and Li, Q. (2019). Pedestrian network generation based on crowdsourced tracking data. Int. J. Geogr. Inf. Sci.","DOI":"10.1080\/13658816.2019.1702197"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"71965","DOI":"10.1109\/ACCESS.2018.2882581","article-title":"Automatic Generation of Road Maps from Low Quality GPS Trajectory Data via Structure Learning","volume":"6","author":"Huang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, S., Wang, Y., and Li, Y. (2015, January 3\u20136). Efficient map reconstruction and augmentation via topological methods. Proceedings of the 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA.","DOI":"10.1145\/2820783.2820833"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, D., Li, J., and Li, J. (2019). Road Network Extraction from Low-Frequency Trajectories Based on a Road Structure-Aware Filter. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8090374"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.isprsjprs.2012.01.002","article-title":"Mathematical morphology-based generalization of complex 3D building models incorporating semantic relationships","volume":"68","author":"Zhao","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1080\/13658816.2014.997238","article-title":"Pattern-mining approach for conflating crowdsourcing road networks with POIs","volume":"29","author":"Yang","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/4\/186\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:11:16Z","timestamp":1760173876000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/4\/186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,25]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["ijgi9040186"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9040186","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,25]]}}}