{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T04:41:24Z","timestamp":1782016884950,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,6,6]],"date-time":"2019-06-06T00:00:00Z","timestamp":1559779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702010"],"award-info":[{"award-number":["61702010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672039"],"award-info":[{"award-number":["61672039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["1808085MF172"],"award-info":[{"award-number":["1808085MF172"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University Natural Science Research Program of Anhui Province","award":["KJ2017A327"],"award-info":[{"award-number":["KJ2017A327"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The results of road congestion detection can be used for the rational planning of travel routes and as guidance for traffic management. The trajectory data of moving objects can record their positions at each moment and reflect their moving features. Utilizing trajectory mining technology to effectively identify road congestion locations is of great importance and has practical value in the fields of traffic and urban planning. This paper addresses the issue by proposing a novel approach to detect road congestion locations based on trajectory stay-place clustering. First, this approach estimates the speed status of each time-stamped location in each trajectory. Then, it extracts the stay places of the trajectory, each of which is denoted as a seven-tuple containing information such as starting and ending time, central coordinate, average direction difference, and so on. Third, the time-stamped locations included in stay places are partitioned into different stay-place equivalence classes according to the timestamps. Finally, stay places in each equivalence class are clustered to mine the congestion locations of multiple trajectories at a certain period of time. Visual representation and experimental results on real-life cab trajectory datasets show that the proposed approach is suitable for the detection of congestion locations at different timestamps.<\/jats:p>","DOI":"10.3390\/ijgi8060264","type":"journal-article","created":{"date-parts":[[2019,6,7]],"date-time":"2019-06-07T03:56:31Z","timestamp":1559879791000},"page":"264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Road Congestion Detection Based on Trajectory Stay-Place Clustering"],"prefix":"10.3390","volume":"8","author":[{"given":"Qingying","family":"Yu","sequence":"first","affiliation":[{"name":"School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China"},{"name":"School of Computer and Information, Anhui Normal University, Wuhu 241002, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, Wuhu 241002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4987-0376","authenticated-orcid":false,"given":"Yonglong","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China"},{"name":"School of Computer and Information, Anhui Normal University, Wuhu 241002, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, Wuhu 241002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanming","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu 241002, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, Wuhu 241002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyao","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu 241002, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, Wuhu 241002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,6]]},"reference":[{"key":"ref_1","first-page":"189","article-title":"Rapid traffic congestion monitoring based on floating car data","volume":"51","author":"Wu","year":"2014","journal-title":"J. Comput. Res. Dev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fang, H., Hsu, W.J., and Rudolph, L. (2009). Mining user position log for construction of personalized activity map. International Conference on Advanced Data Mining and Applications, Springer.","DOI":"10.1007\/978-3-642-03348-3_43"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1109\/TPAMI.2012.188","article-title":"An incremental DPMM-based method for trajectory clustering, modeling, and retrieval","volume":"35","author":"Hu","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Luo, W., Tan, H., Chen, L., and Ni, L.M. (2013, January 22\u201327). Finding time period-based most frequent path in big trajectory data. Proceedings of the ACM SIGMOD International Conference on Management of Data, New York, NY, USA.","DOI":"10.1145\/2463676.2465287"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1145\/2743025","article-title":"Trajectory data mining: An overview","volume":"6","author":"Zheng","year":"2015","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1016\/j.neucom.2015.08.071","article-title":"The discovery of personally semantic places based on trajectory data mining","volume":"173","author":"Lv","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ins.2012.04.015","article-title":"Microaggregation- and permutation-based anonymization of movement data","volume":"208","year":"2012","journal-title":"Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1049\/iet-its.2014.0238","article-title":"Trajectory-based anomalous behaviour detection for intelligent traffic surveillance","volume":"9","author":"Cai","year":"2015","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.1109\/TKDE.2013.160","article-title":"Online discovery of gathering patterns over trajectories","volume":"26","author":"Zheng","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.future.2015.11.013","article-title":"Urban traffic congestion estimation and prediction based on floating car trajectory data","volume":"61","author":"Kong","year":"2016","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1109\/TITS.2017.2697439","article-title":"Antmapper: An ant colony-based map matching approach for trajectory-based applications","volume":"19","author":"Gong","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kami, N., Enomoto, N., Baba, T., and Yoshikawa, T. (2010). Algorithm for detecting significant locations from raw GPS data. Discovery Science, Springer.","DOI":"10.1007\/978-3-642-16184-1_16"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3511","DOI":"10.4028\/www.scientific.net\/AMM.353-356.3511","article-title":"Identifying the stay point using GPS trajectory of taxis","volume":"353\u2013356","author":"Xiao","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zheng, Y., and Zhou, X. (2011). Computing with Spatial Trajectories, Springer.","DOI":"10.1007\/978-1-4614-1629-6"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.trc.2014.08.002","article-title":"Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks","volume":"48","author":"Anbaroglu","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.pmcj.2017.03.015","article-title":"Exploring traffic congestion correlation from multiple data sources","volume":"41","author":"Wang","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.eswa.2016.12.018","article-title":"Detection of traffic congestion and incidents from GPS trace analysis","volume":"73","author":"Marcelloni","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1109\/TVT.2012.2231973","article-title":"Detecting crowdedness spot in city transportation","volume":"62","author":"Liu","year":"2013","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1016\/j.patcog.2017.03.030","article-title":"Locality constraint distance metric learning for traffic congestion detection","volume":"75","author":"Wang","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., Zhang, L., Xie, X., and Sun, G. (2011, January 17\u201321). Where to find my next passenger?. Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China.","DOI":"10.1145\/2030112.2030128"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huo, Z., Meng, X., Hu, H., and Huang, Y. (2012). You can walk alone: Trajectory privacy-preserving through significant stays protection. International Conference on Database Systems for Advanced Applications, Springer.","DOI":"10.1007\/978-3-642-29038-1_26"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Do, T.M.T., and Gatica-Perez, D. (2012, January 5\u20138). Contextual conditional models for smartphone-based human mobility prediction. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370242"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., and Ma, W. (2008, January 5\u20137). Mining user similarity based on location history. Proceedings of the ACM Sigspatial International Conference on Advances in Geographic Information Systems, Irvine, CA, USA.","DOI":"10.1145\/1463434.1463477"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2390","DOI":"10.1109\/TKDE.2012.153","article-title":"T-Finder: A recommender system for finding passengers and vacant taxis","volume":"25","author":"Yuan","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pavan, M., Mizzaro, S., Scagnetto, I., and Beggiato, A. (2015, January 15\u201318). Finding Important Locations: A Feature-Based Approach. Proceedings of the 16th IEEE International Conference on Mobile Data Management, Pittsburgh, PA, USA.","DOI":"10.1109\/MDM.2015.11"},{"key":"ref_26","unstructured":"Stylianou, G. (2017). Stay-point Identification as Curve Extrema. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.14778\/1920841.1920968","article-title":"Mining significant semantic locations from GPS data","volume":"3","author":"Cao","year":"2010","journal-title":"Proc. Vldb Endow."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.ins.2016.06.033","article-title":"Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data","volume":"373","author":"An","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.1007\/s10489-017-1104-z","article-title":"Trajectory outlier detection approach based on common slices sub-sequence","volume":"48","author":"Yu","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_30","unstructured":"Piorkowski, M., Sarafijanovic-Djukic, N., and Grossglauser, M. (2019, April 16). CRAWDAD dataset epfl\/mobility(v. 2009-02-24). Available online: http:\/\/crawdad.org\/epfl\/mobility\/20090224."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Piorkowski, M., Sarafijanovic-Djukic, N., and Grossglauser, M. (2009, January 5\u201310). A parsimonious model of mobile partitioned networks with clustering. Proceedings of the 1st International Conference on Communication Systems and NETworks, Bangalore, India.","DOI":"10.1109\/COMSNETS.2009.4808865"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1921591.1921596","article-title":"Recommending friends and locations based on individual location history","volume":"5","author":"Zheng","year":"2011","journal-title":"ACM Trans. Web."},{"key":"ref_33","unstructured":"Han, J., Kamber, M., and Pei, J. (2013). Data Mining: Concepts and Techniques, Morgan Kaufmann."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/6\/264\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:56:35Z","timestamp":1760187395000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/6\/264"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,6]]},"references-count":33,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["ijgi8060264"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8060264","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,6]]}}}