{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T22:54:37Z","timestamp":1780095277631,"version":"3.54.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s12652-022-03708-0","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T10:02:43Z","timestamp":1644400963000},"page":"9751-9766","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Temporal prediction of traffic characteristics on real road scenarios in Amman"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4093-9349","authenticated-orcid":false,"given":"Raneem","family":"Qaddoura","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3844-6409","authenticated-orcid":false,"given":"Maram Bani","family":"Younes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"issue":"9","key":"3708_CR1","doi-asserted-by":"publisher","first-page":"2435","DOI":"10.1109\/TITS.2016.2641903","volume":"18","author":"R Al Mallah","year":"2017","unstructured":"Al Mallah R, Quintero A, Farooq B (2017) Distributed classification of urban congestion using vanet. IEEE Trans Intell Transp Syst 18(9):2435\u20132442","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"9","key":"3708_CR2","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1145\/361002.361007","volume":"18","author":"JL Bentley","year":"1975","unstructured":"Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509\u2013517","journal-title":"Commun ACM"},{"key":"3708_CR3","unstructured":"Bhatia N, et\u00a0al. (2010) Survey of nearest neighbor techniques. arXiv preprint arXiv:1007.0085"},{"key":"3708_CR4","unstructured":"Botchkarev A (2018) Performance metrics (error measures) in machine learning regression, forecasting and prognostics: properties and typology. arXiv preprint arXiv:1809.03006"},{"key":"3708_CR5","doi-asserted-by":"crossref","unstructured":"Dao MS, Nguyen NT, Zettsu K (2019) Multi-time-horizon traffic risk prediction using spatio-temporal urban sensing data fusion. In: 2019 IEEE international conference on big data (big data), IEEE, pp 2205\u20132214","DOI":"10.1109\/BigData47090.2019.9005524"},{"issue":"4","key":"3708_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3337064","volume":"52","author":"S Fletcher","year":"2019","unstructured":"Fletcher S, Islam MZ (2019) Decision tree classification with differential privacy: a survey. ACM Comput Surv (CSUR) 52(4):1\u201333","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"7","key":"3708_CR7","doi-asserted-by":"publisher","first-page":"709","DOI":"10.3390\/e21070709","volume":"21","author":"Z Huang","year":"2019","unstructured":"Huang Z, Xia J, Li F, Li Z, Li Q (2019) A peak traffic congestion prediction method based on bus driving time. Entropy 21(7):709","journal-title":"Entropy"},{"key":"3708_CR8","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.compeleceng.2018.04.017","volume":"68","author":"S Khan","year":"2018","unstructured":"Khan S, Alam M, Fr\u00e4nzle M, M\u00fcllner N, Chen Y (2018) A traffic aware segment-based routing protocol for vanets in urban scenarios. Comput Electric Eng 68:447\u2013462","journal-title":"Comput Electric Eng"},{"key":"3708_CR9","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.future.2015.11.013","volume":"61","author":"X Kong","year":"2016","unstructured":"Kong X, Xu Z, Shen G, Wang J, Yang Q, Zhang B (2016) Urban traffic congestion estimation and prediction based on floating car trajectory data. Futur Gener Comput Syst 61:97\u2013107","journal-title":"Futur Gener Comput Syst"},{"key":"3708_CR10","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/978-1-4419-6142-6_7","volume-title":"Fundamentals of traffic simulation","author":"D Krajzewicz","year":"2010","unstructured":"Krajzewicz D (2010) Traffic simulation with sumo-simulation of urban mobility. Fundamentals of traffic simulation. Springer, New York, pp 269\u2013293"},{"key":"3708_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.inffus.2018.09.013","volume":"49","author":"DP Kumar","year":"2019","unstructured":"Kumar DP, Amgoth T, Annavarapu CSR (2019) Machine learning algorithms for wireless sensor networks: a survey. Inf Fusion 49:1\u201325","journal-title":"Inf Fusion"},{"key":"3708_CR12","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1016\/j.sbspro.2014.07.259","volume":"138","author":"Y Liu","year":"2014","unstructured":"Liu Y, Feng X, Wang Q, Zhang H, Wang X (2014) Prediction of urban road congestion using a Bayesian network approach. Procedia Soc Behav Sci 138:671\u2013678","journal-title":"Procedia Soc Behav Sci"},{"key":"3708_CR13","doi-asserted-by":"crossref","unstructured":"Lopez PA, Behrisch M, Bieker-Walz L, Erdmann J, Fl\u00f6tter\u00f6d YP, Hilbrich R, L\u00fccken L, Rummel J, Wagner P, Wie\u00dfner E (2018) Microscopic traffic simulation using sumo. In: The 21st IEEE international conference on intelligent transportation systems, IEEE, https:\/\/elib.dlr.de\/124092\/","DOI":"10.1109\/ITSC.2018.8569938"},{"issue":"2","key":"3708_CR14","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1109\/TITS.2015.2491365","volume":"17","author":"P Lopez-Garcia","year":"2015","unstructured":"Lopez-Garcia P, Onieva E, Osaba E, Masegosa AD, Perallos A (2015) A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy. IEEE Trans Intell Transp Syst 17(2):557\u2013569","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"4","key":"3708_CR15","doi-asserted-by":"publisher","first-page":"818","DOI":"10.3390\/s17040818","volume":"17","author":"X Ma","year":"2017","unstructured":"Ma X, Dai Z, He Z, Ma J, Wang Y, Wang Y (2017) Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4):818","journal-title":"Sensors"},{"issue":"17","key":"3708_CR16","doi-asserted-by":"publisher","first-page":"2907","DOI":"10.1002\/wcm.2729","volume":"16","author":"G Martuscelli","year":"2016","unstructured":"Martuscelli G, Boukerche A, Foschini L, Bellavista P (2016) V2v protocols for traffic congestion discovery along routes of interest in vanets: a quantitative study. Wirel Commun Mob Comput 16(17):2907\u20132923","journal-title":"Wirel Commun Mob Comput"},{"key":"3708_CR17","unstructured":"Marwah B, Singh B (2000) Level of service classification for urban heterogeneous traffic: A case study of Kanpur metropolis. In: fourth international symposium on Highway Capacity, Hawaii"},{"key":"3708_CR18","doi-asserted-by":"crossref","unstructured":"Milojevic M, Rakocevic V (2014) Distributed road traffic congestion quantification using cooperative vanets. In: 2014 13th annual Mediterranean ad hoc networking workshop (MED-HOC-NET), IEEE, pp 203\u2013210","DOI":"10.1109\/MedHocNet.2014.6849125"},{"key":"3708_CR19","volume-title":"Introduction to linear regression analysis","author":"DC Montgomery","year":"2021","unstructured":"Montgomery DC, Peck EA, Vining GG (2021) Introduction to linear regression analysis. Wiley, New York"},{"key":"3708_CR20","doi-asserted-by":"crossref","unstructured":"More R, Mugal A, Rajgure S, Adhao RB, Pachghare VK (2016) Road traffic prediction and congestion control using artificial neural networks. In: 2016 international conference on computing, analytics and security trends (CAST), IEEE, pp 52\u201357","DOI":"10.1109\/CAST.2016.7914939"},{"key":"3708_CR21","doi-asserted-by":"crossref","unstructured":"Nguyen NT, Dao MS, Zettsu K (2020) Leveraging 3d-raster-images and deepcnn with multi-source urban sensing data for traffic congestion prediction. In: International conference on database and expert systems applications. Springer, New York, pp 396\u2013406","DOI":"10.1007\/978-3-030-59051-2_27"},{"key":"3708_CR22","doi-asserted-by":"crossref","unstructured":"Othman MSB, Keoh SL, Tan G (2017) Efficient journey planning and congestion prediction through deep learning. In: 2017 international smart cities conference (ISC2), IEEE, pp 1\u20136","DOI":"10.1109\/ISC2.2017.8090805"},{"key":"3708_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"issue":"6","key":"3708_CR24","doi-asserted-by":"publisher","first-page":"1608","DOI":"10.3390\/s20061608","volume":"20","author":"D Polap","year":"2020","unstructured":"Polap D, Wlodarczyk-Sielicka M (2020) Classification of non-conventional ships using a neural bag-of-words mechanism. Sensors 20(6):1608","journal-title":"Sensors"},{"key":"3708_CR25","doi-asserted-by":"crossref","unstructured":"Po\u0142ap D, W\u0142odarczyk-Sielicka M, Wawrzyniak N (2021) Automatic ship classification for a riverside monitoring system using a cascade of artificial intelligence techniques including penalties and rewards. ISA transactions","DOI":"10.1016\/j.isatra.2021.04.003"},{"issue":"145","key":"3708_CR26","first-page":"158","volume":"52","author":"MA Poole","year":"1971","unstructured":"Poole MA, O\u2019Farrell PN (1971) The assumptions of the linear regression model. Trans Inst Brit Geogr 52:145\u2013158","journal-title":"Trans Inst Brit Geogr"},{"issue":"3","key":"3708_CR27","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s13042-019-01027-z","volume":"11","author":"R Qaddoura","year":"2020","unstructured":"Qaddoura R, Faris H, Aljarah I (2020a) An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio. Int J Mach Learn Cybern 11(3):675\u2013714","journal-title":"Int J Mach Learn Cybern"},{"key":"3708_CR28","doi-asserted-by":"crossref","unstructured":"Qaddoura R, Faris H, Aljarah I, Guerv\u00f3s JJM, Castillo PA (2020b) Empirical evaluation of distance measures for nearest point with indexing ratio clustering algorithm. In: IJCCI, pp 430\u2013438","DOI":"10.5220\/0010121504300438"},{"issue":"8","key":"3708_CR29","doi-asserted-by":"publisher","first-page":"8387","DOI":"10.1007\/s12652-020-02570-2","volume":"12","author":"R Qaddoura","year":"2021","unstructured":"Qaddoura R, Faris H, Aljarah I (2021a) An efficient evolutionary algorithm with a nearest neighbor search technique for clustering analysis. J Ambient Intell Humaniz Comput 12(8):8387\u20138412","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"3708_CR44","doi-asserted-by":"crossref","unstructured":"Qaddoura R, Bani Younes M, Boukerche A (2021b) Predicting traffic characteristics of real road scenarios in Jordan and Gulf region. In: Proceedings of the 17th ACM symposium on QoS and security for wireless and mobile networks, pp 115\u2013121","DOI":"10.1145\/3479242.3487329"},{"issue":"11","key":"3708_CR30","first-page":"131","volume":"3","author":"M Raheem","year":"2014","unstructured":"Raheem M, Okereke O (2014) A neural network approach to GSM traffic congestion prediction. Am J Eng Res 3(11):131\u2013138","journal-title":"Am J Eng Res"},{"key":"3708_CR31","doi-asserted-by":"crossref","unstructured":"Shaw S, Prakash M (2019) Solar radiation forecasting using support vector regression. In: 2019 International conference on advances in computing and communication engineering (ICACCE), IEEE, pp 1\u20134","DOI":"10.1109\/ICACCE46606.2019.9080008"},{"issue":"7","key":"3708_CR32","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.3390\/en13071765","volume":"13","author":"V Shepelev","year":"2020","unstructured":"Shepelev V, Aliukov S, Nikolskaya K, Shabiev S (2020) The capacity of the road network: data collection and statistical analysis of traffic characteristics. Energies 13(7):1765","journal-title":"Energies"},{"key":"3708_CR33","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.neucom.2017.04.018","volume":"251","author":"Y Song","year":"2017","unstructured":"Song Y, Liang J, Lu J, Zhao X (2017) An efficient instance selection algorithm for k nearest neighbor regression. Neurocomputing 251:26\u201334","journal-title":"Neurocomputing"},{"key":"3708_CR34","doi-asserted-by":"crossref","unstructured":"Toncharoen R, Piantanakulchai M (2018) Traffic state prediction using convolutional neural network. In: 2018 15th international joint conference on computer science and software engineering (JCSSE), IEEE, pp 1\u20136","DOI":"10.1109\/JCSSE.2018.8457359"},{"key":"3708_CR35","doi-asserted-by":"publisher","first-page":"57311","DOI":"10.1109\/ACCESS.2018.2873569","volume":"6","author":"FH Tseng","year":"2018","unstructured":"Tseng FH, Hsueh JH, Tseng CW, Yang YT, Chao HC, Chou LD (2018) Congestion prediction with big data for real-time highway traffic. IEEE Access 6:57311\u201357323","journal-title":"IEEE Access"},{"key":"3708_CR36","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.cie.2019.03.020","volume":"130","author":"F Wen","year":"2019","unstructured":"Wen F, Zhang G, Sun L, Wang X, Xu X (2019) A hybrid temporal association rules mining method for traffic congestion prediction. Comput Ind Eng 130:779\u2013787","journal-title":"Comput Ind Eng"},{"issue":"3","key":"3708_CR37","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.rse.2005.05.008","volume":"97","author":"M Xu","year":"2005","unstructured":"Xu M, Watanachaturaporn P, Varshney PK, Arora MK (2005) Decision tree regression for soft classification of remote sensing data. Remote Sens Environ 97(3):322\u2013336","journal-title":"Remote Sens Environ"},{"key":"3708_CR38","doi-asserted-by":"crossref","unstructured":"Yan H, Yu DJ (2017) Short-term traffic condition prediction of urban road network based on improved svm. In: 2017 international smart cities conference (ISC2), IEEE, pp 1\u20132","DOI":"10.1109\/ISC2.2017.8090856"},{"issue":"8","key":"3708_CR39","first-page":"1","volume":"12","author":"MB Younes","year":"2020","unstructured":"Younes MB (2020) Real-time traffic distribution prediction protocol (TDPP) for vehicular networks. J Ambient Intell Human Comput 12(8):1\u201312","journal-title":"J Ambient Intell Human Comput"},{"key":"3708_CR40","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.adhoc.2014.09.005","volume":"24","author":"MB Younes","year":"2015","unstructured":"Younes MB, Boukerche A (2015) A performance evaluation of an efficient traffic congestion detection protocol (ecode) for intelligent transportation systems. Ad Hoc Netw 24:317\u2013336","journal-title":"Ad Hoc Netw"},{"issue":"5","key":"3708_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3403952","volume":"53","author":"MB Younes","year":"2020","unstructured":"Younes MB, Boukerche A (2020) Traffic efficiency applications over downtown roads: A new challenge for intelligent connected vehicles. ACM Comput Surv (CSUR) 53(5):1\u201330","journal-title":"ACM Comput Surv (CSUR)"},{"key":"3708_CR42","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.trc.2014.02.013","volume":"43","author":"X Zhang","year":"2014","unstructured":"Zhang X, Onieva E, Perallos A, Osaba E, Lee VC (2014) Hierarchical fuzzy rule-based system optimized with genetic algorithms for short term traffic congestion prediction. Transport Res Part C Emerg Technol 43:127\u2013142","journal-title":"Transport Res Part C Emerg Technol"},{"issue":"9","key":"3708_CR43","doi-asserted-by":"publisher","first-page":"168781401666738","DOI":"10.1177\/1687814016667384","volume":"8","author":"HH Zhang","year":"2016","unstructured":"Zhang HH, Jiang CP, Yang L (2016) Forecasting traffic congestion status in terminal areas based on support vector machine. Adv Mech Eng 8(9):1687814016667384","journal-title":"Adv Mech Eng"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-03708-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-022-03708-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-03708-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T21:52:13Z","timestamp":1686088333000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-022-03708-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,9]]},"references-count":44,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["3708"],"URL":"https:\/\/doi.org\/10.1007\/s12652-022-03708-0","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,9]]},"assertion":[{"value":"22 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}