{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T18:09:05Z","timestamp":1789150145859,"version":"build-2803163510"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2020,7,24]],"date-time":"2020-07-24T00:00:00Z","timestamp":1595548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,24]],"date-time":"2020-07-24T00:00:00Z","timestamp":1595548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s00521-020-05196-9","type":"journal-article","created":{"date-parts":[[2020,7,24]],"date-time":"2020-07-24T17:03:03Z","timestamp":1595610183000},"page":"3311-3324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Real-time signal queue length prediction using long short-term memory neural network"],"prefix":"10.1007","volume":"33","author":[{"given":"Rezaur","family":"Rahman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5828-3352","authenticated-orcid":false,"given":"Samiul","family":"Hasan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,24]]},"reference":[{"key":"5196_CR1","unstructured":"Schrank D, Eisele B, Lomax T, Bak J (2015) Urban mobility scorecard. Texas A&M Transportation Institute, vol 39, August, 2015. https:\/\/doi.org\/DTRT06-G-0044"},{"key":"5196_CR2","doi-asserted-by":"crossref","unstructured":"Smith SF, Barlow GJ, Xie X-F, Rubinstein ZB (2013) Smart urban signal networks: initial application of the SURTRAC adaptive traffic signal control system. In: Icaps, pp 434\u2013442","DOI":"10.1609\/icaps.v23i1.13594"},{"key":"5196_CR3","unstructured":"Chandra R, Gregory C (2012) InSync adaptive traffic signal technology: real-time artificial intelligence delivering real-world results. In: InSync White Paper, No. March 2012"},{"issue":"4","key":"5196_CR4","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1016\/j.trc.2009.02.003","volume":"17","author":"HX Liu","year":"2009","unstructured":"Liu HX, Wu X, Ma W, Hu H (2009) Real-time queue length estimation for congested signalized intersections. Transp Res Part C: Emerg Technol 17(4):412\u2013427. https:\/\/doi.org\/10.1016\/j.trc.2009.02.003","journal-title":"Transp Res Part C: Emerg Technol"},{"key":"5196_CR5","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.trc.2015.01.007","volume":"55","author":"Y Feng","year":"2015","unstructured":"Feng Y, Head KL, Khoshmagham S, Zamanipour M (2015) A real-time adaptive signal control in a connected vehicle environment. Transp Res Part C: Emerg Technol 55:460\u2013473. https:\/\/doi.org\/10.1016\/j.trc.2015.01.007","journal-title":"Transp Res Part C: Emerg Technol"},{"key":"5196_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/info8030101","author":"P Jing","year":"2017","unstructured":"Jing P, Huang H, Chen L (2017) An adaptive traffic signal control in a connected vehicle environment: a systematic review. Information (Switzerland). https:\/\/doi.org\/10.3390\/info8030101","journal-title":"Information (Switzerland)"},{"issue":"6","key":"5196_CR7","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/S0968-090X(00)00047-4","volume":"9","author":"P Mirchandani","year":"2001","unstructured":"Mirchandani P, Head L (2001) A real-time traffic signal control system: architecture, algorithms, and analysis. Transportation Research Part C: Emerging Technologies 9(6):415\u2013432. https:\/\/doi.org\/10.1016\/S0968-090X(00)00047-4","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"5196_CR8","unstructured":"Miyato T, Dai AM, Goodfellow I (2019) Adversarial training methods for semi-supervised text classification. In: 5th international conference on learning representations, ICLR 2017\u2014conference track proceedings, pp 1\u201311"},{"key":"5196_CR9","unstructured":"Rosen-Zvi M, Griffiths T, Steyvers M, Smyth P (2012) The author-topic model for authors and documents. arXiv preprint arXiv:1207.4169"},{"key":"5196_CR10","doi-asserted-by":"publisher","DOI":"10.1145\/1177352.1177355","author":"A Yilmaz","year":"2006","unstructured":"Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv. https:\/\/doi.org\/10.1145\/1177352.1177355","journal-title":"ACM Comput Surv"},{"key":"5196_CR11","unstructured":"Finn C, Goodfellow I, Levine S (2016). Unsupervised learning for physical interaction through video prediction. In: Advances in neural information processing systems, No Nips, pp 64\u201372"},{"issue":"4","key":"5196_CR12","doi-asserted-by":"publisher","first-page":"1763","DOI":"10.1109\/TASE.2019.2895801","volume":"16","author":"J Bi","year":"2019","unstructured":"Bi J, Yuan H, Zhou M (2019) Temporal Prediction of Multiapplication Consolidated Workloads in Distributed Clouds. IEEE Trans Autom Sci Eng 16(4):1763\u20131773. https:\/\/doi.org\/10.1109\/TASE.2019.2895801","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"5196_CR13","first-page":"155","volume":"1","author":"H Drucker","year":"1997","unstructured":"Drucker H, Surges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. Adv Neural Inf Process Syst 1:155\u2013161","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"5196_CR14","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks 1: introduction. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"issue":"3","key":"5196_CR15","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","volume":"46","author":"NS Altman","year":"1992","unstructured":"Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175\u2013185","journal-title":"Am Stat"},{"issue":"10","key":"5196_CR16","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/5.58337","volume":"78","author":"PJ Werbos","year":"1990","unstructured":"Werbos PJ (1990) Backpropagation through time: what it does and how to do it. Proc IEEE 78(10):1550\u20131560. https:\/\/doi.org\/10.1109\/5.58337","journal-title":"Proc IEEE"},{"issue":"April","key":"5196_CR17","doi-asserted-by":"publisher","first-page":"2254","DOI":"10.1201\/9780429500459","volume":"79","author":"JJ Hopfield","year":"1982","unstructured":"Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79(April):2254\u20132558. https:\/\/doi.org\/10.1201\/9780429500459","journal-title":"Proc Natl Acad Sci USA"},{"key":"5196_CR18","first-page":"1977","volume-title":"Time series analysis: forecasting and control","author":"M Geurts","year":"1977","unstructured":"Geurts M, Box GEP, Jenkins GM (1977) Time series analysis: forecasting and control. Wiley, New York, p 1977"},{"issue":"6","key":"5196_CR19","doi-asserted-by":"publisher","first-page":"04016018","DOI":"10.1061\/(ASCE)TE.1943-5436.0000816","volume":"142","author":"B Yu","year":"2016","unstructured":"Yu B, Song X, Guan F, Yang Z, Yao B (2016) K-nearest neighbor model for multiple-time-step prediction of short-term traffic condition. J Transp Eng 142(6):04016018. https:\/\/doi.org\/10.1061\/(ASCE)TE.1943-5436.0000816","journal-title":"J Transp Eng"},{"key":"5196_CR20","doi-asserted-by":"publisher","unstructured":"Deshpande M, Bajaj PR (2016) Performance analysis of support vector machine for traffic flow prediction. In: 2016 international conference on global trends in signal processing, information computing and communication (ICGTSPICC), 2016, pp 126\u2013129. https:\/\/doi.org\/10.1109\/ICGTSPICC.2016.7955283","DOI":"10.1109\/ICGTSPICC.2016.7955283"},{"key":"5196_CR21","doi-asserted-by":"publisher","unstructured":"Wu C, Wei C, Su D, Chang M, Ho J (2004). Travel time prediction with support vector regression. In: Proceedings of the 2003 IEEE international conference on intelligent transportation systems, vol 2, pp 1438\u20131442. https:\/\/doi.org\/10.1109\/ITSC.2003.1252721","DOI":"10.1109\/ITSC.2003.1252721"},{"key":"5196_CR22","doi-asserted-by":"crossref","unstructured":"Billings D, Jiann-Shiou Y (2006) Application of the ARIMA models to urban roadway travel time prediction\u2014a case study. In: IEEE international conference on systems, man and cybernetics (SMC\u201906) 2006, pp 2529\u20132534","DOI":"10.1109\/ICSMC.2006.385244"},{"key":"5196_CR23","unstructured":"Lee YLY (2009) Freeway travel time forecast using artifical neural networks with cluster method. In: 2009 12th international conference on information fusion, pp 1331\u20131338"},{"issue":"2","key":"5196_CR24","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s005210050015","volume":"8","author":"R Yasdi","year":"1999","unstructured":"Yasdi R (1999) Prediction of road traffic using a neural network approach. Neural Comput Appl 8(2):135\u2013142. https:\/\/doi.org\/10.1007\/s005210050015","journal-title":"Neural Comput Appl"},{"issue":"3","key":"5196_CR25","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/s00521-010-0456-7","volume":"21","author":"WC Hong","year":"2012","unstructured":"Hong WC (2012) Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting. Neural Comput Appl 21(3):583\u2013593. https:\/\/doi.org\/10.1007\/s00521-010-0456-7","journal-title":"Neural Comput Appl"},{"issue":"7\u20138","key":"5196_CR26","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1007\/s00521-014-1646-5","volume":"25","author":"VJ Hodge","year":"2014","unstructured":"Hodge VJ, Krishnan R, Austin J, Polak J, Jackson T (2014) Short-term prediction of traffic flow using a binary neural network. Neural Comput Appl 25(7\u20138):1639\u20131655. https:\/\/doi.org\/10.1007\/s00521-014-1646-5","journal-title":"Neural Comput Appl"},{"key":"5196_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.trc.2017.02.024","volume":"79","author":"NG Polson","year":"2017","unstructured":"Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C: Emerg Technol 79:1\u201317. https:\/\/doi.org\/10.1016\/j.trc.2017.02.024","journal-title":"Transp Res Part C: Emerg Technol"},{"issue":"5","key":"5196_CR28","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.1007\/s00521-018-3470-9","volume":"31","author":"Y Ai","year":"2019","unstructured":"Ai Y, Li Z, Gan M, Zhang Y, Yu D, Chen W, Ju Y (2019) A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Comput Appl 31(5):1665\u20131677. https:\/\/doi.org\/10.1007\/s00521-018-3470-9","journal-title":"Neural Comput Appl"},{"key":"5196_CR29","doi-asserted-by":"publisher","unstructured":"Shukla S, Balachandran K, Sumitha VS (2017) A Framework for smart transportation using big data. In: Proceedings of 2016 international conference on ICT in business, industry, and government, ICTBIG 2016, pp 1\u20133. https:\/\/doi.org\/10.1109\/ICTBIG.2016.7892720","DOI":"10.1109\/ICTBIG.2016.7892720"},{"key":"5196_CR30","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","volume":"54","author":"X Ma","year":"2015","unstructured":"Ma X, Tao Z, Wang Y, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp Res Part C: Emerg Technol 54:187\u2013197. https:\/\/doi.org\/10.1016\/j.trc.2015.03.014","journal-title":"Transp Res Part C: Emerg Technol"},{"issue":"7","key":"5196_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s17071501","volume":"17","author":"H Yu","year":"2017","unstructured":"Yu H, Wu Z, Wang S, Wang Y, Ma X (2017) Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors (Switzerland) 17(7):1\u201316. https:\/\/doi.org\/10.3390\/s17071501","journal-title":"Sensors (Switzerland)"},{"key":"5196_CR32","doi-asserted-by":"publisher","unstructured":"Rahman R, Hasan S (2018). Short-term traffic speed prediction for freeways during hurricane evacuation: a deep learning approach. pp 1291\u20131296. https:\/\/doi.org\/10.1109\/ITSC.2018.8569443","DOI":"10.1109\/ITSC.2018.8569443"},{"issue":"2","key":"5196_CR33","first-page":"223","volume":"7","author":"GF Newell","year":"1965","unstructured":"Newell GF (1965) Approximation methods for queues with application to the fixed-cycle traffic light. Soc Ind Appl Math 7(2):223\u2013240","journal-title":"Soc Ind Appl Math"},{"issue":"6","key":"5196_CR34","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/S0191-2615(99)00034-X","volume":"34","author":"TH Chang","year":"2000","unstructured":"Chang TH, Lin JT (2000) Optimal signal timing for an oversaturated intersection. Transp Res Part B: Methodol 34(6):471\u2013491. https:\/\/doi.org\/10.1016\/S0191-2615(99)00034-X","journal-title":"Transp Res Part B: Methodol"},{"issue":"1","key":"5196_CR35","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TITS.2006.888619","volume":"8","author":"PB Mirchandani","year":"2007","unstructured":"Mirchandani PB, Zou N (2007) Queuing models for analysis of traffic adaptive signal control. IEEE Trans Intell Transp Syst 8(1):50\u201359. https:\/\/doi.org\/10.1109\/TITS.2006.888619","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5196_CR36","unstructured":"Balke KN, Charara H, Parker R (2005). Development of a traffic signal performance measurement system (TSPMS). vol 7, No May 2005, p 83"},{"key":"5196_CR37","unstructured":"Webster FV (1957) Traffic signal settings. Road research technical paper 39"},{"key":"5196_CR38","unstructured":"Robertson D (1969) TRANSYT: a traffic network study tool ministry of transport. RRL Report LR253, Crowthorne, Berkshire, United Kingdom"},{"key":"5196_CR39","first-page":"290","volume":"1965","author":"AD May","year":"1975","unstructured":"May AD (1975) Traffic flow theory- the traffic engineers challenge. Proc Inst Traf Eng 1965:290\u2013303","journal-title":"Proc Inst Traf Eng"},{"key":"5196_CR40","doi-asserted-by":"publisher","first-page":"69","DOI":"10.3141\/2035-08","volume":"2035","author":"A Sharma","year":"2007","unstructured":"Sharma A, Bullock DM, Bonneson JA, Sharma A, Bullock DM, Bonneson JA (2007) Input\u2013output and hybrid techniques for real-time prediction of delay and maximum queue length at signalized intersections delay and maximum queue length at signalized intersections. Transp Res Record: J Transp Res Board 2035:69\u201380. https:\/\/doi.org\/10.3141\/2035-08","journal-title":"Transp Res Record: J Transp Res Board"},{"issue":"1","key":"5196_CR41","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.trc.2007.06.002","volume":"16","author":"G Vigos","year":"2008","unstructured":"Vigos G, Papageorgiou M, Wang Y (2008) Real-time estimation of vehicle-count within signalized links. Transp Res Part C: Emerg Technol 16(1):18\u201335. https:\/\/doi.org\/10.1016\/j.trc.2007.06.002","journal-title":"Transp Res Part C: Emerg Technol"},{"issue":"5","key":"5196_CR42","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/0191-2607(79)90028-1","volume":"13","author":"G Stephanopoulos","year":"1979","unstructured":"Stephanopoulos G, Michalopoulos PG, Stephanopoulos G (1979) Modelling and analysis of traffic queue dynamics at signalized intersections. Transp Res Part A: Gen 13(5):295\u2013307. https:\/\/doi.org\/10.1016\/0191-2607(79)90028-1","journal-title":"Transp Res Part A: Gen"},{"issue":"1178","key":"5196_CR43","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1098\/rspa.1955.0089","volume":"229","author":"MJ Lighthill","year":"1955","unstructured":"Lighthill MJ, Whitham GB (1955) On kinematic waves II a theory of traffic flow on long crowded roads. Proc R Soc Lond Ser A Math Phys Sci 229(1178):317\u2013345. https:\/\/doi.org\/10.1098\/rspa.1955.0089","journal-title":"Proc R Soc Lond Ser A Math Phys Sci"},{"issue":"1","key":"5196_CR44","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1287\/opre.4.1.42","volume":"4","author":"PI Richards","year":"1956","unstructured":"Richards PI (1956) Shock waves on the highway. Oper Res 4(1):42\u201351. https:\/\/doi.org\/10.1287\/opre.4.1.42","journal-title":"Oper Res"},{"issue":"2035","key":"5196_CR45","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3141\/2035-11","volume":"2035","author":"E Smaglik","year":"2007","unstructured":"Smaglik E, Sharma A, Bullock D, Sturdevant J, Duncan G (2007) Event-based data collection for generating actuated controller performance measures. Transp Res Record: J Transp Res Board 2035(2035):97\u2013106. https:\/\/doi.org\/10.3141\/2035-11","journal-title":"Transp Res Record: J Transp Res Board"},{"key":"5196_CR46","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2017.1299011","author":"C An","year":"2017","unstructured":"An C, Wu YJ, Xia J, Huang W (2017) Real-time queue length estimation using event-based advance detector data. J Intell Transp Syst Technol Plan Oper. https:\/\/doi.org\/10.1080\/15472450.2017.1299011","journal-title":"J Intell Transp Syst Technol Plan Oper"},{"issue":"6","key":"5196_CR47","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1016\/j.trc.2011.01.002","volume":"19","author":"X Jeff Ban","year":"2011","unstructured":"Jeff Ban X, Hao P, Sun Z (2011) Real time queue length estimation for signalized intersections using travel times from mobile sensors. Transp Res Part C: Emerg Technol 19(6):1133\u20131156. https:\/\/doi.org\/10.1016\/j.trc.2011.01.002","journal-title":"Transp Res Part C: Emerg Technol"},{"key":"5196_CR48","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.trb.2015.10.002","volume":"82","author":"P Hao","year":"2015","unstructured":"Hao P, Ban X (2015) Long queue estimation for signalized intersections using mobile data. Transp Res Part B: Methodol 82:54\u201373. https:\/\/doi.org\/10.1016\/j.trb.2015.10.002","journal-title":"Transp Res Part B: Methodol"},{"key":"5196_CR49","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.trb.2013.05.001","volume":"55","author":"G Comert","year":"2013","unstructured":"Comert G (2013) Simple analytical models for estimating the queue lengths from probe vehicles at traffic signals. Transp Res Part B: Methodol 55:59\u201374. https:\/\/doi.org\/10.1016\/j.trb.2013.05.001","journal-title":"Transp Res Part B: Methodol"},{"issue":"4","key":"5196_CR50","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1109\/TITS.2015.2401007","volume":"16","author":"K Tiaprasert","year":"2015","unstructured":"Tiaprasert K, Zhang Y, Wang XB, Zeng X (2015) Queue length estimation using connected vehicle technology for adaptive signal control. IEEE Trans Intell Transp Syst 16(4):2129\u20132140. https:\/\/doi.org\/10.1109\/TITS.2015.2401007","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"4","key":"5196_CR51","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s40534-019-00200-y","volume":"27","author":"A Emami","year":"2019","unstructured":"Emami A, Sarvi M, Asadi Bagloee S (2019) A neural network algorithm for queue length estimation based on the concept of K-leader connected vehicles. J Modern Transp 27(4):341\u2013354. https:\/\/doi.org\/10.1007\/s40534-019-00200-y","journal-title":"J Modern Transp"},{"issue":"9","key":"5196_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s19092059","volume":"19","author":"K Gao","year":"2019","unstructured":"Gao K, Han F, Dong P, Xiong N, Du R (2019) Connected vehicle as a mobile sensor for real time queue length at signalized intersections. Sensors (Switzerland) 19(9):1\u201322. https:\/\/doi.org\/10.3390\/s19092059","journal-title":"Sensors (Switzerland)"},{"issue":"3","key":"5196_CR53","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/0968-090X(95)00005-4","volume":"3","author":"GL Chang","year":"1995","unstructured":"Chang GL, Su CC (1995) Predicting intersection queue with neural network models. Transp Res Part C 3(3):175\u2013191. https:\/\/doi.org\/10.1016\/0968-090X(95)00005-4","journal-title":"Transp Res Part C"},{"key":"5196_CR54","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.trc.2019.10.011","volume":"109","author":"S Lee","year":"2019","unstructured":"Lee S, Xie K, Ngoduy D, Keyvan-Ekbatani M (2019) An advanced deep learning approach to real-time estimation of lane-based queue lengths at a signalized junction. Transp Res Part C: Emerg Technol 109:117\u2013136. https:\/\/doi.org\/10.1016\/j.trc.2019.10.011","journal-title":"Transp Res Part C: Emerg Technol"},{"issue":"2","key":"5196_CR55","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1109\/TITS.2017.2700209","volume":"19","author":"S Gan","year":"2018","unstructured":"Gan S, Liang S, Li K, Deng J, Cheng T (2018) Trajectory length prediction for intelligent traffic signaling: a data-driven approach. IEEE Trans Intell Transp Syst 19(2):426\u2013435. https:\/\/doi.org\/10.1109\/TITS.2017.2700209","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"1","key":"5196_CR56","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/JAS.2019.1911348","volume":"6","author":"J Wang","year":"2019","unstructured":"Wang J, Kumbasar T (2019) Parameter optimization of interval Type-2 fuzzy neural networks based on PSO and BBBC methods. IEEE\/CAA J Autom Sin 6(1):247\u2013257. https:\/\/doi.org\/10.1109\/JAS.2019.1911348","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"2","key":"5196_CR57","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/TNNLS.2018.2846646","volume":"30","author":"S Gao","year":"2019","unstructured":"Gao S, Zhou M, Wang Y, Cheng J, Yachi H, Wang J (2019) Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE Trans Neural Netw Learn Syst 30(2):601\u2013614. https:\/\/doi.org\/10.1109\/TNNLS.2018.2846646","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"5196_CR58","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1109\/JAS.2017.7510469","volume":"5","author":"J Zeng","year":"2018","unstructured":"Zeng J, Yu S, Qian Y, Feng X (2018) Expressway traffic flow model study based on different traffic rules. IEEE\/CAA J Autom Sin 5(6):1099\u20131103. https:\/\/doi.org\/10.1109\/JAS.2017.7510469","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"5196_CR59","unstructured":"Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning 17 Oct 2015. pp 1\u201338. arXiv:1506.00019v4 [Cs.LG]"},{"issue":"8","key":"5196_CR60","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Urgen Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"5196_CR61","volume-title":"Hands-on machine learning with Scikit-learn and TensorFlow","author":"A G\u00e9ron","year":"2017","unstructured":"G\u00e9ron A (2017) Hands-on machine learning with Scikit-learn and TensorFlow. O\u2019Reilly Media, Newton"},{"issue":"2","key":"5196_CR62","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1109\/TASE.2018.2865663","volume":"16","author":"G Wang","year":"2019","unstructured":"Wang G, Qiao J, Bi J, Li W, Zhou M (2019) TL-GDBN: growing deep belief network with transfer learning. IEEE Trans Autom Sci Eng 16(2):874\u2013885. https:\/\/doi.org\/10.1109\/TASE.2018.2865663","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"5196_CR63","unstructured":"Bergstra J, Bardenet R, Bengio Y, K\u00e9gl B (2011) Algorithms for hyper-parameter optimization. In: Advances in neural information processing systems (NIPS), pp 2546\u20132554. 2012arXiv1206.2944S"},{"key":"5196_CR64","doi-asserted-by":"publisher","unstructured":"Bergstra J, Yamins D, Cox DD (2013) Hyperopt: a Python library for optimizing the hyperparameters of machine learning algorithms. In: 12th python in science conference (SCIPY 2013), No. Scipy, pp 13\u201320. https:\/\/doi.org\/10.1088\/1749-4699\/8\/1\/014008","DOI":"10.1088\/1749-4699\/8\/1\/014008"},{"key":"5196_CR65","doi-asserted-by":"publisher","unstructured":"Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration. Lecture notes in computer science (including subseries. Lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 6683 LNCS, 2011, pp 507\u2013523. https:\/\/doi.org\/10.1007\/978-3-642-25566-3_40","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"5196_CR66","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1023\/A:1012771025575","volume":"21","author":"DR Jones","year":"2001","unstructured":"Jones DR (2001) A taxonomy of global optimization methods based on response surfaces. J Global Optim 21:345\u2013383. https:\/\/doi.org\/10.1023\/A:1012771025575","journal-title":"J Global Optim"},{"key":"5196_CR67","unstructured":"Rahman R (2019) Applications of deep learning models for traffic prediction problems. In: Electronic theses and dissertations, 2004\u20132019, p 6286. https:\/\/stars.library.ucf.edu\/etd\/6286"},{"key":"5196_CR68","unstructured":"TDOT (2020) Traffic design manual. Traffic operations division traffic engineering office. https:\/\/www.tn.gov\/tdot\/traffic-operationsdivision\/traffic-operations-division-resources\/traffic-design-manual.html"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05196-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05196-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05196-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T19:23:53Z","timestamp":1723317833000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05196-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,24]]},"references-count":68,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5196"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05196-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,24]]},"assertion":[{"value":"8 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}