{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:03:55Z","timestamp":1760241835982,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,9,23]],"date-time":"2018-09-23T00:00:00Z","timestamp":1537660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The General Program of National Natural Science Foundation of China","award":["11772301"],"award-info":[{"award-number":["11772301"]}]},{"name":"The Project of Natural Science Foundation of Zhejiang Pvovince,China","award":["LY17F020016"],"award-info":[{"award-number":["LY17F020016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Nowadays, most vehicles are equipped with positioning devices such as GPS which can generate a tremendous amount of trajectory data and upload them to the server in real time. The trajectory data can reveal the shape and evolution of the road network and therefore has an important value for road planning, vehicle navigation, traffic analysis, and so on. In this paper, a road network generation method is proposed based on the incremental learning of vehicle trajectories. Firstly, the input vehicle trajectory data are cleaned by a preprocess module. Then, the original scattered positions are clustered and mapped to the representation points which stand for the feature points of the real roads. After that, the corresponding representation points are connected based on the original connection information of the trajectories. Finally, all representation points are connected by a Delaunay triangulation network and the real road segments are found by a shortest path searching approach between the connected representation point pairs. Experiments show that this method can build the road network from scratch and refine it with the input data continuously. Both the accuracy and timeliness of the extracted road network can continuously be improved with the growth of real-time trajectory data.<\/jats:p>","DOI":"10.3390\/ijgi7100382","type":"journal-article","created":{"date-parts":[[2018,9,24]],"date-time":"2018-09-24T10:38:49Z","timestamp":1537785529000},"page":"382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Incremental Road Network Generation Based on Vehicle Trajectories"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9139-3713","authenticated-orcid":false,"given":"Zhongyi","family":"Ni","sequence":"first","affiliation":[{"name":"School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Xie","sequence":"additional","affiliation":[{"name":"Zhejiang Lab, Hangzhou 311121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binhua","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2126-2897","authenticated-orcid":false,"given":"Yao","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,23]]},"reference":[{"key":"ref_1","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, WA, USA.","DOI":"10.1145\/1653771.1653776"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4144","DOI":"10.1109\/TGRS.2007.906107","article-title":"Road network extraction and intersection detection from aerial images by tracking road footprints","volume":"45","author":"Hu","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1134\/S1054661806020118","article-title":"Automated extraction of road network from medium-and high-resolution images","volume":"16","author":"Zanin","year":"2006","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1109\/JSTARS.2015.2449296","article-title":"Road extraction from very high resolution remote sensing optical images based on texture analysis and beamlet transform","volume":"9","author":"Sghaier","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s10708-007-9111-y","article-title":"Citizens as sensors: The world of volunteered geography","volume":"69","author":"Goodchild","year":"2007","journal-title":"GeoJournal"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"593","DOI":"10.14358\/PERS.82.8.593","article-title":"Automatic extraction of road networks from GPS traces","volume":"82","author":"Qiu","year":"2016","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Niu, Z., Li, S., and Pousaeid, N. (2011, January 23\u201325). Road extraction using smart phones GPS. Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications, Washington, DC, USA.","DOI":"10.1145\/1999320.1999342"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, H., Kulik, L., and Ramamohanarao, K. (2016, January 24\u201328). Automatic generation and validation of road maps from GPS trajectory data sets. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, Indianapolis, IN, USA.","DOI":"10.1145\/2983323.2983797"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Klein, R., Six, H.-W., and Wegner, L. (2003). Route planning and map inference with global positioning traces. Computer Science in Perspective, Springer.","DOI":"10.1007\/3-540-36477-3"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, C., and Cheng, Y. (2008, January 21\u201322). Roads digital map generation with multi-track GPS data. Proceedings of the IEEE International Workshop on Education Technology and Training, 2008 and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008, Shanghai, China.","DOI":"10.1109\/ETTandGRS.2008.70"},{"key":"ref_11","unstructured":"Charikar, M. (2010, January 17\u201319). Road network reconstruction for organizing paths. Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms, Austin, TX, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, L., Thiemann, F., and Sester, M. (2010, January 2). Integration of GPS traces with road map. Proceedings of the ACM Third International Workshop on Computational Transportation Science, San Jose, CA, USA.","DOI":"10.1145\/1899441.1899447"},{"key":"ref_13","unstructured":"Bruntrup, R., Edelkamp, S., Jabbar, S., and Scholz, B. (2005, January 16). Incremental map generation with GPS traces. Proceedings of the IEEE Intelligent Transportation Systems, Vienna, Austria."},{"key":"ref_14","unstructured":"Ahmed, M., and Wenk, C. Constructing street networks from GPS trajectories. European Symposium on Algorithms, Proceedings of the 20th Annual European Conference on Algorithms, Ljubljana, Slovenia, 10\u201312 September 2012."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fabrikant, S.I., Reichenbacher, T., van Kreveld, M., and Schlieder, C. (2010). Detecting road intersections from GPS traces. Geographic Information Science, Proceedings of the 6th International Conference on Geographic Information Science, Zurich, Switzerland, 14\u201317 September 2010, Springer.","DOI":"10.1007\/978-3-642-15300-6"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Karagiorgou, S., and Pfoser, D. (2012, January 6\u20139). On vehicle tracking data-based road network generation. Proceedings of the ACM 20th International Conference on Advances in Geographic Information Systems, Redondo Beach, CA, USA.","DOI":"10.1145\/2424321.2424334"},{"key":"ref_17","first-page":"97","article-title":"Trajectory big data: Data, applications and techniques","volume":"36","author":"Xu","year":"2015","journal-title":"J. Commun."},{"key":"ref_18","first-page":"959","article-title":"Trajectory big data: A review of key technologies in data processing. RuanJian Xue Bao","volume":"28","author":"Gao","year":"2017","journal-title":"J. Softw."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zheng, Y., and Zhou, X. (2011). Computing with Spatial Trajectories, Springer Science & Business Media.","DOI":"10.1007\/978-1-4614-1629-6"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.neucom.2008.11.032","article-title":"Extracting road information from recorded GPS data using snap-drift neural network","volume":"73","author":"Ekpenyong","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/BF00977785","article-title":"Two algorithms for constructing a Delaunay triangulation","volume":"9","author":"Lee","year":"1980","journal-title":"Int. J. Comput. Inf. Sci."},{"key":"ref_22","unstructured":"Cormen, T.H., Leiserson, C.E., Rivest, R.L., and Stein, C. (2001). Introduction to Algorithms, The Massachusetts Institute of Technology. [2nd ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1093\/comjnl\/24.2.167","article-title":"Computing the n-dimensional Delaunay tessellation with application to Voronoi polytopes","volume":"24","author":"Watson","year":"1981","journal-title":"Comput. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/S0098-3004(03)00017-7","article-title":"Delete and insert operations in Voronoi\/Delaunay methods and applications","volume":"29","author":"Mostafavi","year":"2003","journal-title":"Comput. Geosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"61","DOI":"10.3141\/2291-08","article-title":"Inferring road maps from global positioning system traces: Survey and comparative evaluation","volume":"2291","author":"Biagioni","year":"2012","journal-title":"Transport. Res. Rec."},{"key":"ref_26","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 ACM 20th International Conference on Advances in Geographic Information Systems, Redondo Beach, CA, USA.","DOI":"10.1145\/2424321.2424333"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/10\/382\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:22:08Z","timestamp":1760196128000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/10\/382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,23]]},"references-count":26,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["ijgi7100382"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7100382","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2018,9,23]]}}}