{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:24:48Z","timestamp":1770351888371,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T00:00:00Z","timestamp":1670025600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T00:00:00Z","timestamp":1670025600000},"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":["Int. J. ITS Res."],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s13177-022-00332-2","type":"journal-article","created":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T09:03:28Z","timestamp":1670058208000},"page":"26-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Optimized Deep Neural Network Based Intelligent Decision Support System for Traffic State Prediction"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8211-8453","authenticated-orcid":false,"given":"D.","family":"Deva Hema","sequence":"first","affiliation":[]},{"given":"K. Ashok","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,3]]},"reference":[{"key":"332_CR1","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1109\/TITS.2011.2158001","volume":"12","author":"J Zhang","year":"2011","unstructured":"Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., Chen, C.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 12, 1624\u20131639 (2011). https:\/\/doi.org\/10.1109\/TITS.2011.2158001","journal-title":"IEEE Trans. Intell. Transp. Syst"},{"key":"332_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/713930748","volume":"7","author":"B Abdulhai","year":"2002","unstructured":"Abdulhai, B., Porwal, H., Recker, W.: Short-term traffic flow prediction using neuro-genetic algorithms. J. Intell. Transp. Syst. 7, 3\u201341 (2002). https:\/\/doi.org\/10.1080\/713930748","journal-title":"J. Intell. Transp. Syst"},{"key":"332_CR3","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.trb.2011.09.008","volume":"46","author":"L Du","year":"2012","unstructured":"Du, L., Peeta, S., Kim, Y.H.: An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks. Transp. Res. Part. B Methodol. 46, 235\u2013252 (2012). https:\/\/doi.org\/10.1016\/j.trb.2011.09.008","journal-title":"Transp. Res. Part. B Methodol"},{"key":"332_CR4","doi-asserted-by":"publisher","first-page":"28","DOI":"10.3141\/2175-04","volume":"2175","author":"J Guo","year":"2010","unstructured":"Guo, J., Williams, B.M.: Real-time short-term traffic speed level forecasting and uncertainty quantification using layered Kalman Filters. Transp. Res. Rec J. Transp. Res. Board. 2175, 28\u201337 (2010). https:\/\/doi.org\/10.3141\/2175-04","journal-title":"Transp. Res. Rec J. Transp. Res. Board"},{"key":"332_CR5","doi-asserted-by":"publisher","first-page":"179","DOI":"10.3141\/1678-22","volume":"1678","author":"S Lee","year":"1999","unstructured":"Lee, S., Fambro, D.B.: Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transp. Res. Rec J. Transp. Res. Board. 1678, 179\u2013188 (1999). https:\/\/doi.org\/10.3141\/1678-22","journal-title":"Transp. Res. Rec J. Transp. Res. Board"},{"key":"332_CR6","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","volume":"129","author":"BM Williams","year":"2003","unstructured":"Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J. Transp. Eng. 129, 664\u2013672 (2003). https:\/\/doi.org\/10.1061\/(ASCE)0733-947X(2003)129:6(664)","journal-title":"J. Transp. Eng"},{"key":"332_CR7","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0968-090X(97)82903-8","volume":"4","author":"M Van Der Voort","year":"1996","unstructured":"Van Der Voort, M., Dougherty, M., Watson, S.: Combining kohonen maps with arima time series models to forecast traffic flow. Transp. Res. Part. C Emerg. Technol. 4, 307\u2013318 (1996). https:\/\/doi.org\/10.1016\/S0968-090X(97)82903-8","journal-title":"Transp. Res. Part. C Emerg. Technol"},{"key":"332_CR8","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1109\/TITS.2011.2174051","volume":"13","author":"KY Chan","year":"2012","unstructured":"Chan, K.Y., Dillon, T.S., Singh, J., Chang, E.: Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg\u2013Marquardt Algorithm. IEEE Trans. Intell. Transp. Syst. 13, 644\u2013654 (2012). https:\/\/doi.org\/10.1109\/TITS.2011.2174051","journal-title":"IEEE Trans. Intell. Transp. Syst"},{"key":"332_CR9","doi-asserted-by":"publisher","first-page":"6164","DOI":"10.1016\/j.eswa.2008.07.069","volume":"36","author":"M Castro-Neto","year":"2009","unstructured":"Castro-Neto, M., Jeong, Y.-S., Jeong, M.-K., Han, L.D.: Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst. Appl. 36, 6164\u20136173 (2009). https:\/\/doi.org\/10.1016\/j.eswa.2008.07.069","journal-title":"Expert Syst. Appl"},{"key":"332_CR10","doi-asserted-by":"publisher","unstructured":"Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.-Y.:\u00a0Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 1\u20139 (2014). https:\/\/doi.org\/10.1109\/TITS.2014.2345663\u00a0","DOI":"10.1109\/TITS.2014.2345663"},{"key":"332_CR11","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.trc.2018.03.001","volume":"90","author":"Y Wu","year":"2018","unstructured":"Wu, Y., Tan, H., Qin, L., Ran, B., Jiang, Z.: A hybrid deep learning based traffic flow prediction method and its understanding. Transp. Res. Part. C Emerg. Technol. 90, 166\u2013180 (2018). https:\/\/doi.org\/10.1016\/j.trc.2018.03.001","journal-title":"Transp. Res. Part. C Emerg. Technol"},{"key":"332_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s13177-021-00273-2","author":"D Deva Hema","year":"2021","unstructured":"Deva Hema, D., Ashok Kumar, K.: Levenberg\u2013Marquardt \u2013LSTM based efficient rear\u2013end crash risk prediction system optimization. Int. J. Intell. Transp. Syst. Res. (2021). https:\/\/doi.org\/10.1007\/s13177-021-00273-2","journal-title":"Int. J. Intell. Transp. Syst. Res"},{"key":"332_CR13","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.1109\/TITS.2020.2983763","volume":"22","author":"M Lv","year":"2021","unstructured":"Lv, M., Hong, Z., Chen, L., Chen, T., Zhu, T., Ji, S.: Temporal multi-graph convolutional network for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 22, 3337\u20133348 (2021). https:\/\/doi.org\/10.1109\/TITS.2020.2983763","journal-title":"IEEE Trans. Intell. Transp. Syst"},{"key":"332_CR14","doi-asserted-by":"publisher","first-page":"3776","DOI":"10.1109\/TITS.2020.3025856","volume":"22","author":"C Chen","year":"2021","unstructured":"Chen, C., Liu, Z., Wan, S., Luan, J., Pei, Q.: Traffic flow prediction based on deep learning in internet of vehicles. IEEE Trans. Intell. Transp. Syst. 22, 3776\u20133789 (2021). https:\/\/doi.org\/10.1109\/TITS.2020.3025856","journal-title":"IEEE Trans. Intell. Transp. Syst"},{"key":"332_CR15","doi-asserted-by":"crossref","unstructured":"Huang, W., Hong, H., Li, M., Hu, W., Song, G., Xie, K.: Deep architecture for traffic flow prediction. International Conference on Advanced Data Mining and Applications 165\u2013176 (2013)","DOI":"10.1007\/978-3-642-53917-6_15"},{"key":"332_CR16","doi-asserted-by":"publisher","unstructured":"Zheng, H., Lin, F., Feng, X., Chen, Y.: A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic Flow Prediction. IEEE Trans. Intell. Transp. Syst. 1\u201311 (2020). https:\/\/doi.org\/10.1109\/TITS.2020.2997352","DOI":"10.1109\/TITS.2020.2997352"},{"key":"332_CR17","doi-asserted-by":"crossref","unstructured":"Tian, Y., Pan, L.: Predicting short-term traffic flow by long short-term memory recurrent neural network. In: 2015 IEEE International Conference on Smart City\/SocialCom\/SustainCom (SmartCity). pp. 153\u2013158. IEEE (2015)","DOI":"10.1109\/SmartCity.2015.63"},{"key":"332_CR18","doi-asserted-by":"crossref","unstructured":"\u0141ukasik, S., Kowalski, P.A., Charytanowicz, M., Kulczycki, P.: Data Clustering with Grasshopper Optimization Algorithm. Presented at the September 24 (2017)","DOI":"10.15439\/2017F340"},{"key":"332_CR19","doi-asserted-by":"publisher","first-page":"114576","DOI":"10.1016\/j.eswa.2021.114576","volume":"171","author":"P-H Dinh","year":"2021","unstructured":"Dinh, P.-H.: A novel approach based on Grasshopper optimization algorithm for medical image fusion. Expert Syst. Appl. 171, 114576 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.114576","journal-title":"Expert Syst. Appl"},{"key":"332_CR20","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.knosys.2017.12.037","volume":"145","author":"M Mafarja","year":"2018","unstructured":"Mafarja, M., Aljarah, I., Heidari, A.A., Hammouri, A.I., Faris, H., Al-Zoubi, A.M., Mirjalili, S.: Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl.-Based Syst. 145, 25\u201345 (2018). https:\/\/doi.org\/10.1016\/j.knosys.2017.12.037","journal-title":"Knowl.-Based Syst."},{"key":"332_CR21","doi-asserted-by":"publisher","first-page":"105515","DOI":"10.1016\/j.asoc.2019.105515","volume":"81","author":"AK Bhandari","year":"2019","unstructured":"Bhandari, A.K., Rahul, K.: A novel local contrast fusion-based fuzzy model for color image multilevel thresholding using grasshopper optimization. Appl. Soft Comput. 81, 105515 (2019). https:\/\/doi.org\/10.1016\/j.asoc.2019.105515","journal-title":"Appl. Soft Comput"},{"key":"332_CR22","doi-asserted-by":"crossref","unstructured":"Bairathi, D., Gopalani, D.: An improved opposition based grasshopper optimisation algorithm for numerical optimization. In: International Conference on Intelligent Systems Design and Applications. pp. 843\u2013851. Springer (2018)","DOI":"10.1007\/978-3-030-16660-1_82"},{"key":"332_CR23","unstructured":"Hall, F.L.: Traffic stream characteristics, Traffic flow theory\u2013A state-of-the-art report (Washington DC). Transp. Res. Board.\u00a036,\u00a0(1992)"},{"key":"332_CR24","doi-asserted-by":"publisher","first-page":"e0176853","DOI":"10.1371\/journal.pone.0176853","volume":"12","author":"A Ermagun","year":"2017","unstructured":"Ermagun, A., Chatterjee, S., Levinson, D.: Using temporal detrending to observe the spatial correlation of traffic. PLoS ONE. 12, e0176853 (2017). https:\/\/doi.org\/10.1371\/journal.pone.0176853","journal-title":"PLoS ONE"},{"key":"332_CR25","doi-asserted-by":"publisher","first-page":"159773","DOI":"10.1109\/ACCESS.2020.3020356","volume":"8","author":"A Bala","year":"2020","unstructured":"Bala, A., Ismail, I., Ibrahim, R., Sait, S.M., Oliva, D.: An improved grasshopper optimization algorithm based echo state network for predicting faults in airplane engines. IEEE Access 8, 159773\u2013159789 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3020356","journal-title":"IEEE Access"},{"key":"332_CR26","doi-asserted-by":"publisher","first-page":"155429","DOI":"10.1109\/ACCESS.2020.3019048","volume":"8","author":"Y Shi","year":"2020","unstructured":"Shi, Y., Li, Y., Fan, J., Wang, T., Yin, T.: A novel network architecture of decision-making for self-driving vehicles based on long short-term memory and grasshopper optimization algorithm. IEEE Access 8, 155429\u2013155440 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3019048","journal-title":"IEEE Access"},{"key":"332_CR27","doi-asserted-by":"crossref","unstructured":"Hema, D.D.: D.K.A.K.: Hyperparameter optimization of LSTM based driver\u2019s aggressive behavior prediction model. In: International Conference on Artificial Intelligence and Smart Systems (ICAIS 2021). pp. 751\u2013756. IEEE, Coimbatore (2021)","DOI":"10.1109\/ICAIS50930.2021.9396047"},{"key":"332_CR28","doi-asserted-by":"publisher","first-page":"4227","DOI":"10.3233\/JIFS-169980","volume":"36","author":"A Veeramuthu","year":"2019","unstructured":"Veeramuthu, A., Meenakshi, S., Ashok Kumar, K.: A neural network based deep learning approach for efficient segmentation of brain tumor medical image data. J. Intell. Fuzzy Syst. 36, 4227\u20134234 (2019). https:\/\/doi.org\/10.3233\/JIFS-169980","journal-title":"J. Intell. Fuzzy Syst"},{"key":"332_CR29","doi-asserted-by":"crossref","unstructured":"Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC). pp. 324\u2013328. IEEE (2016)","DOI":"10.1109\/YAC.2016.7804912"},{"issue":"4","key":"332_CR30","first-page":"1","volume":"43","author":"D Deva Hema","year":"2022","unstructured":"Deva Hema, D., Kumar, A.: Novel algorithm for multivariate time series crash risk prediction using CNN-ATT-LSTM model. J. Intell. Fuzzy Syst. 43(4), 1\u201313 (2022)","journal-title":"J. Intell. Fuzzy Syst."},{"key":"332_CR31","unstructured":"Manual, H.C.: Highway capacity manual. Wash. DC 2(1), (2000)"},{"key":"332_CR32","doi-asserted-by":"publisher","unstructured":"Chen, X., Chen, Y., He, Z.: Urban Traffic Speed Dataset of Guangzhou, China (2018). https:\/\/doi.org\/10.5281\/zenodo.1205229,","DOI":"10.5281\/zenodo.1205229"},{"key":"332_CR33","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.trc.2018.11.003","volume":"98","author":"X Chen","year":"2019","unstructured":"Chen, X., He, Z., Sun, L.: A bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transp. Res. Part. C Emerg. Technol. 98, 73\u201384 (2019)","journal-title":"Transp. Res. Part. C Emerg. Technol"},{"key":"332_CR34","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.trc.2017.10.023","volume":"86","author":"X Chen","year":"2018","unstructured":"Chen, X., He, Z., Wang, J.: Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition. Transp. Res. Part. C Emerg. Technol. 86, 59\u201377 (2018)","journal-title":"Transp. Res. Part. C Emerg. Technol"},{"key":"332_CR35","doi-asserted-by":"publisher","first-page":"143025","DOI":"10.1109\/ACCESS.2019.2941280","volume":"7","author":"G Dai","year":"2019","unstructured":"Dai, G., Ma, C., Xu, X.: Short-term traffic flow prediction method for urban road sections based on space\u2013time analysis and GRU. IEEE Access 7, 143025\u2013143035 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2941280","journal-title":"IEEE Access"}],"container-title":["International Journal of Intelligent Transportation Systems Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-022-00332-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13177-022-00332-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-022-00332-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T07:44:09Z","timestamp":1679643849000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13177-022-00332-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,3]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["332"],"URL":"https:\/\/doi.org\/10.1007\/s13177-022-00332-2","relation":{},"ISSN":["1348-8503","1868-8659"],"issn-type":[{"value":"1348-8503","type":"print"},{"value":"1868-8659","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,3]]},"assertion":[{"value":"10 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We have no conflicts of interest to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}