{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:08:31Z","timestamp":1772302111263,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T00:00:00Z","timestamp":1662508800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T00:00:00Z","timestamp":1662508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"The Science, Technology & Innovation Funding Authority"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Egypt has\u00a0been fighting the issue of ensuring road safety\u201a reducing accidents\u201a preserving the lives of citizens since its inception. For these reasons\u201a precisely identifying the road condition\u201a followed by effective and timely maintenance and rehabilitation measures\u201a leads to an increase in the road network's safety level and lifespan. This paper presents a multi-input deep learning framework that combines BiLSTM and Depthwise separable convolution to work in parallel for automatic recognition of road surface quality and different road anomalies. Furthermore, we performed an investigation to compare deep networks approaches against other traditional approaches using real-time data sensed and collected from the Egyptian road network. The proposed deep model has achieved an average accuracy of 93.1%\u201a which is superior compared to other evaluated approaches. Finally, we utilized the proposed model to estimate a road quality index in the Egyptian cities.<\/jats:p>","DOI":"10.1007\/s00521-022-07736-x","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T17:08:09Z","timestamp":1662570489000},"page":"2927-2944","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["IoT for measuring road network quality index"],"prefix":"10.1007","volume":"35","author":[{"given":"E.","family":"Raslan","sequence":"first","affiliation":[]},{"given":"Mohammed F.","family":"Alrahmawy","sequence":"additional","affiliation":[]},{"given":"Y. A.","family":"Mohammed","sequence":"additional","affiliation":[]},{"given":"A. S.","family":"Tolba","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"7736_CR1","doi-asserted-by":"publisher","unstructured":"Chun Chanjun, Ryu Seung-Ki (2019) Road surface damage detection using fully convolutional neural networks and semi-supervised learning. Sensors (Basel\u201a Switzerland). 19. https:\/\/doi.org\/10.3390\/s19245501","DOI":"10.3390\/s19245501"},{"key":"7736_CR2","doi-asserted-by":"publisher","first-page":"4231","DOI":"10.1109\/JSEN.2017.2702739","volume":"17","author":"A Allouch","year":"2017","unstructured":"Allouch A\u201a Koubaa A\u201a Abbes T\u201a Ammar A (2017) RoadSense: smartphone application to estimate road conditions using accelerometer and gyroscope.\u00a0IEEE Sensors J 17:4231\u20134238","journal-title":"IEEE Sensors J"},{"issue":"12","key":"7736_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s18124342","volume":"18","author":"J Park","year":"2018","unstructured":"Park J\u201a Min K, Kim H\u201a Lee W\u201a Cho G\u201a Huh K (2018) Road surface classification using a deep ensemble network with sensor feature selection. Sensors 18(12):1\u201316","journal-title":"Sensors"},{"issue":"8","key":"7736_CR4","doi-asserted-by":"publisher","first-page":"3044","DOI":"10.3390\/s22083044","volume":"22","author":"E Ranyal","year":"2022","unstructured":"Ranyal E, Sadhu A, Jain K (2022) Road condition monitoring using smart sensing and artificial intelligence: a review. Sensors 22(8):3044","journal-title":"Sensors"},{"key":"7736_CR5","doi-asserted-by":"publisher","first-page":"2635","DOI":"10.1109\/JSEN.2019.2952857","volume":"20","author":"A Basavaraju","year":"2020","unstructured":"Basavaraju A\u201a Du J\u201a Zhou F\u201a Ji J (2020) A machine learning approach to road surface anomaly assessment using smartphone sensors.\u00a0IEEE Sensors J 20:2635\u20132647","journal-title":"IEEE Sensors J"},{"issue":"4","key":"7736_CR6","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"H Ismail Fawaz","year":"2019","unstructured":"Ismail Fawaz H\u201a Forestier G\u201a Weber J\u201a Idoumghar L\u201a Muller PA (2019) Deep learning for time series classification: a review. Data Mining Knowl Discovery\u00a033(4): 917\u2013963","journal-title":"Data Mining Knowl Discovery"},{"issue":"1","key":"7736_CR7","doi-asserted-by":"publisher","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","volume":"8","author":"Z Che","year":"2018","unstructured":"Che Z, Purushotham S, Cho K, Sontag D, Liu Y (2018) Recurrent neural networks for multivariate time series with missing values. Sci Rep 8(1):6085","journal-title":"Sci Rep"},{"key":"7736_CR8","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/S0925-2312(01)00706-8","volume":"50","author":"M Husken","year":"2003","unstructured":"Husken M, Stagge P (2003) Recurrent neural networks for time series \u201cclassification\u201d. Neurocomputing 50:223\u2013235","journal-title":"Neurocomputing"},{"key":"7736_CR9","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1109\/ACCESS.2017.2779939","volume":"6","author":"F Karim","year":"2017","unstructured":"Karim F, Majumdar S, Darabi H, Chen S (2017) Lstm fully convolutional networks for time series classification. IEEE Access 6:1662\u20131669","journal-title":"IEEE Access"},{"key":"7736_CR10","unstructured":"Gamboa JCB (2017) Deep learning for time-series analysis.\u00a0arXiv preprint arXiv:1701.01887"},{"issue":"6","key":"7736_CR11","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","volume":"34","author":"H Ismail Fawaz","year":"2020","unstructured":"Ismail Fawaz H, Lucas B, Forestier G, Pelletier C, Schmidt DF, Weber J, Webb GI, Idoumghar L, Muller PA, Petitjean F (2020) Inceptiontime: finding alexnet for time series classification. Data Min Knowl Disc 34(6):1936\u20131962","journal-title":"Data Min Knowl Disc"},{"issue":"6","key":"7736_CR12","doi-asserted-by":"publisher","first-page":"4788","DOI":"10.1109\/TIE.2018.2864702","volume":"66","author":"C Liu","year":"2019","unstructured":"Liu C, Hsaio W, Tu Y (2019) Time series classification with multivariate convolutional neural network. IEEE Trans Industr Electron 66(6):4788\u20134797. https:\/\/doi.org\/10.1109\/TIE.2018.2864702","journal-title":"IEEE Trans Industr Electron"},{"key":"7736_CR13","unstructured":"Wen Q, Zhou T, Zhang C, Chen W, Ma Z, Yan J, Sun L (2022) Transformers in time series: a survey.\u00a0arXiv preprint arXiv:2202.07125"},{"key":"7736_CR14","unstructured":"Bello Salau H\u201a Onumanyi AJ\u201a Aibinu AM\u201a Onwuka EN\u201a Dukiya JJ, Ohize H (2019) A survey of accelerometer based techniques for road anomalies detection and characterization. Int J Eng Sci Appl 3(1) (in press)"},{"key":"7736_CR15","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s00779-019-01234-z.","volume":"24","author":"B Varona","year":"2020","unstructured":"Varona B, Monteserin A, Teyseyre A (2020) A deep learning approach to automatic road surface monitoring and pothole detection. Pers Ubiquit Comput 24:519\u2013534. https:\/\/doi.org\/10.1007\/s00779-019-01234-z.","journal-title":"Pers Ubiquit Comput"},{"key":"7736_CR16","unstructured":"Setiawan BD\u201a Kryssanov VV\u201a Serd\u00fclt U\u201a Loshchilov A\u201a Mahmudy WF\u201a Nurwasito H (2020) Monitoring road surface conditions with cyclist's smartphone sensors. In: CEUR workshop proceedings\u00a0(No. 2627\u201a pp. 76\u201382). CEUR-WS"},{"key":"7736_CR17","doi-asserted-by":"publisher","unstructured":"Bello-Salau H\u201a Aibinu AM\u201a Onumanyi AJ\u201a Onwuka EN\u201a Dukiya JJ\u201a Ohize H (2018) New Road anomaly detection and characterization algorithm for autonomous vehicles. Appl Comput Informatics. https:\/\/doi.org\/10.1016\/j.aci.2018.05.002","DOI":"10.1016\/j.aci.2018.05.002"},{"issue":"9","key":"7736_CR18","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.1177\/03611981211006105","volume":"2675","author":"C Kyriakou","year":"2021","unstructured":"Kyriakou C, Christodoulou SE, Dimitriou L (2021) Spatial roadway condition-assessment mapping utilizing smartphones and machine learning algorithms. Transp Res Rec 2675(9):1118\u20131126","journal-title":"Transp Res Rec"},{"key":"7736_CR19","doi-asserted-by":"crossref","unstructured":"Alam MY\u201a Nandi A\u201a Kumar A\u201a Saha S\u201a Saha M\u201a Nandi S\u201a Chakraborty S (2020)\u00a0Crowdsourcing from the true crowd: device\u201a vehicle\u201a road-surface\u201a and driving independent road profiling from smartphone sensors. Pervasive Mobile Comput 61:101103","DOI":"10.1016\/j.pmcj.2019.101103"},{"key":"7736_CR20","unstructured":"Setiawan BD\u201a Kryssanov VV, Serd\u00fclt U (2020) Monitoring road surface conditions with cyclist's smartphone sensors.\u00a0IICST"},{"key":"7736_CR21","doi-asserted-by":"crossref","unstructured":"El-Kady A\u201a Emara K\u201a ElEliemy MH\u201a Shaaban E (2019) Road surface quality detection using smartphone sensors: Egyptian roads case study. In:\u00a02019 Ninth international conference on intelligent computing and information systems (ICICIS)\u00a0(pp. 202\u2013207). IEEE, New York","DOI":"10.1109\/ICICIS46948.2019.9014721"},{"key":"7736_CR22","unstructured":"MadisKariler (2017) Road surface quality detection using accelerometer data. Bachelor\u2019s Thesis (9 Ects)\u201a AmnirHidachi\u201a University of Tartu"},{"key":"7736_CR23","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.pmcj.2017.06.002","volume":"40","author":"G Singh","year":"2017","unstructured":"Singh G\u201a Bansal D\u201a Sofat S\u201a Aggarwal N (2017) Smart patrolling: an efficient road surface monitoring using smartphone sensors and crowdsourcing. Pervasive Mobile Comput 40:71\u201388","journal-title":"Pervasive Mobile Comput"},{"key":"7736_CR24","doi-asserted-by":"crossref","unstructured":"Gaurav Singal\u201a Anurag Goswami\u201a Suneet Gupta\u201a Tejalal Choudhary (2018) Pitfree: Pot-holes detection on Indian Roads using Mobile Sensors. In: 8th International Advance Computing Conference (IACC) IEEE\u201a pp. 185\u2013190\u201a 2018","DOI":"10.1109\/IADCC.2018.8692120"},{"key":"7736_CR25","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.trpro.2021.01.023","volume":"52","author":"C Kyriakou","year":"2021","unstructured":"Kyriakou C\u201a Christodoulou SE, Dimitriou L (2021)\u00a0Do vehicles sense\u201a detect and locate speed bumps?\u00a0Transport Res Proc 52:203\u2013210","journal-title":"Transport Res Proc"},{"issue":"10","key":"7736_CR26","doi-asserted-by":"publisher","first-page":"3788","DOI":"10.3390\/s22103788","volume":"22","author":"A Martinelli","year":"2022","unstructured":"Martinelli A, Meocci M, Dolfi M, Branzi V, Morosi S, Argenti F, Berzi L, Consumi T (2022) Road surface anomaly assessment using low-cost accelerometers: a machine learning approach. Sensors 22(10):3788","journal-title":"Sensors"},{"key":"7736_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107725","volume":"99","author":"AK Pandey","year":"2022","unstructured":"Pandey AK, Iqbal R, Maniak T, Karyotis C, Akuma S, Palade V (2022) Convolution neural networks for pothole detection of critical road infrastructure. Comput Electr Eng 99:107725","journal-title":"Comput Electr Eng"},{"key":"7736_CR28","doi-asserted-by":"crossref","unstructured":"Middlehurst M, Large J, Bagnall A (2020) The canonical interval forest (CIF) classifier for time series classification. In:\u00a02020 IEEE international conference on big data (Big Data)\u00a0(pp. 188\u2013195). IEEE, New York","DOI":"10.1109\/BigData50022.2020.9378424"},{"key":"7736_CR29","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer P, Leser U (2017) Multivariate time series classification with WEASEL+ MUSE.\u00a0arXiv preprint arXiv:1711.11343","DOI":"10.1145\/3132847.3132980"},{"issue":"4","key":"7736_CR30","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1007\/s10618-019-00633-3","volume":"33","author":"T Le Nguyen","year":"2019","unstructured":"Le Nguyen T, Gsponer S, Ilie I, O\u2019Reilly M, Ifrim G (2019) Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations. Data Min Knowl Disc 33(4):1183\u20131222","journal-title":"Data Min Knowl Disc"},{"issue":"8","key":"7736_CR31","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, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"7736_CR32","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s10618-020-00727-3","volume":"35","author":"AP Ruiz","year":"2021","unstructured":"Ruiz AP, Flynn M, Large J et al (2021) The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Disc 35:401\u2013449","journal-title":"Data Min Knowl Disc"},{"key":"7736_CR33","doi-asserted-by":"crossref","unstructured":"Kumar R, Wiil U (2019) Recent advances in computational intelligence, ser. Studies in Computational Intelligence. Springer, Cham","DOI":"10.1007\/978-3-030-12500-4"},{"key":"7736_CR34","doi-asserted-by":"crossref","unstructured":"Alzubaidi L\u201a Zhang J\u201a Humaidi AJ\u201a Al-Dujaili A\u201a Duan Y\u201a Al-Shamma O\u201a Santamar\u00eda J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts\u201a CNN architectures\u201a challenges\u201a applications\u201a future directions.\u00a0J Big Data\u00a08(1):1\u201374","DOI":"10.1186\/s40537-021-00444-8"},{"key":"7736_CR35","doi-asserted-by":"publisher","first-page":"53540","DOI":"10.1109\/ACCESS.2021.3070646","volume":"9","author":"Y Shavit","year":"2021","unstructured":"Shavit Y, Klein I (2021) Boosting inertial-based human activity recognition with transformers. IEEE Access 9:53540\u201353547","journal-title":"IEEE Access"},{"key":"7736_CR36","doi-asserted-by":"crossref","unstructured":"Bagnall A, Lines J\u201a Bostrom A\u201a Large J\u201a Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances.\u00a0Data Mining Knowl Discovery\u00a031(3):606\u2013660","DOI":"10.1007\/s10618-016-0483-9"},{"key":"7736_CR37","unstructured":"Road\u2019s quality by country, around the world. TheGlobalEconomy.com. (n.d.). Retrieved February 19, 2022, from https:\/\/www.theglobaleconomy.com\/rankings\/roads_quality\/"},{"issue":"1","key":"7736_CR38","doi-asserted-by":"publisher","first-page":"04019036","DOI":"10.1061\/(ASCE)IS.1943-555X.0000512","volume":"26","author":"SM Piryonesi","year":"2020","unstructured":"Piryonesi SM, El-Diraby TE (2020) Data analytics in asset management: cost-effective prediction of the pavement condition index. J Infrastruct Syst 26(1):04019036","journal-title":"J Infrastruct Syst"},{"key":"7736_CR39","doi-asserted-by":"crossref","unstructured":"Abyarjoo F, Barreto A, Cofino J, Ortega FR (2015) Implementing a sensor fusion algorithm for 3D orientation detection with inertial\/magnetic sensors. In:\u00a0Innovations and advances in computing, informatics, systems sciences, networking and engineering\u00a0(pp. 305\u2013310). Springer, Cham","DOI":"10.1007\/978-3-319-06773-5_41"},{"key":"7736_CR40","unstructured":"Brownlee J (2020)\u00a0Imbalanced classification with python: better metrics\u201a balance skewed classes\u201a cost-sensitive learning. Machine Learning Mastery"},{"key":"7736_CR41","doi-asserted-by":"crossref","unstructured":"Ignatius Dimas P\u201a Suhono HS (2020) Assessment on road anomalies using smartphone sensor: a review. In: 2020 International Conference on ICT for Smart Society (ICISS)\u201a CFP2013V-ART\u201a pp 1\u20137","DOI":"10.1109\/ICISS50791.2020.9307603"},{"key":"7736_CR42","doi-asserted-by":"crossref","unstructured":"Smith LN (2017) Cyclical learning rates for training neural networks. In:\u00a02017 IEEE winter conference on applications of computer vision (WACV)\u00a0(pp. 464\u2013472). IEEE, New York","DOI":"10.1109\/WACV.2017.58"},{"key":"7736_CR43","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In\u00a0European conference on computer vision\u00a0(pp. 630\u2013645). Springer, Cham","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"7736_CR44","unstructured":"Tang R, Adhikari A, Lin J (2018) Flops as a direct optimization objective for learning sparse neural networks.\u00a0arXiv preprint arXiv:1811.03060."},{"key":"7736_CR45","unstructured":"Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL (2013) A public domain dataset for human activity recognition using smartphones. In: Esann"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07736-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07736-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07736-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T05:23:21Z","timestamp":1673846601000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07736-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,7]]},"references-count":45,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["7736"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07736-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,7]]},"assertion":[{"value":"16 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}