{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:06:20Z","timestamp":1773795980066,"version":"3.50.1"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001631","name":"University College Dublin","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001631","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The continuous global urbanization with rapid and dynamic transitioning in traffic situations among highly populated cities results in difficulty for data collection and communication. Data collection for millions of vehicles hinders by various problems, i.e., higher cost of energy, time, space, and storage resources. Moreover, higher data traffic results in higher delays, larger throughput, excessive bottlenecks, and frequent repetition of data. To better facilitate the aforementioned challenges and to provide a solution, we have proposed a lightweight Machine Learning based data collection protocol named ML-TDG to effectively deal with higher data volumes in a real-time traffic environment capable of bringing the least burden on the network while utilizing less space, time, and energy. ML-TDG is functional based on Apache Spark, an effective data processing engine that indexes the data based on two logs, i.e., old commuters or frequent\/daily commuters and second new\/occasional commuters. The proposed protocol\u2019s main idea is to utilize real-time traffic, distinguish the indexes in parallel based on two assigned logs criteria to train the network, and collect data with the least sources. For energy and time optimization, dynamic segmentation switching is introduced which is an intelligent road segments division and switching for reducing bottlenecks and replication. ML-TDG is tested and verified on Dublin, Ireland\u2019s busiest motorway M50. ML-TDG performs the data collection, data sorting, and network training to decide the next execution altogether for better optimization every time. The experimental results verify that our proposed protocol is attaining higher performance with lower resource requirements along with rich and time-efficient sustainable data collection clusters in comparison with baseline protocols.<\/jats:p>","DOI":"10.1007\/s40747-023-01241-x","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T03:27:18Z","timestamp":1696562838000},"page":"1879-1897","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine learning based data collection protocol for intelligent transport systems: a real-time implementation on Dublin M50, Ireland"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5741-4122","authenticated-orcid":false,"given":"Maryam","family":"Gillani","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2020-417X","authenticated-orcid":false,"given":"Hafiz Adnan","family":"Niaz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,6]]},"reference":[{"key":"1241_CR1","doi-asserted-by":"crossref","unstructured":"Meena G, Sharma D, Mahrishi M (2020) Traffic prediction for intelligent transportation system using machine learning. In: 2020 3rd international conference on emerging technologies in computer engineering: machine learning and internet of things (ICETCE), IEEE, pp 145\u2013148","DOI":"10.1109\/ICETCE48199.2020.9091758"},{"key":"1241_CR2","doi-asserted-by":"crossref","unstructured":"Kumar R, Kumar P, Tripathi R, Gupta GP, Kumar N, Hassan MM (2021) A privacy-preserving-based secure framework using blockchain-enabled deep-learning in cooperative intelligent transport system. IEEE Trans Intell Transport Syst","DOI":"10.1109\/TITS.2021.3098636"},{"issue":"3","key":"1241_CR3","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.1007\/s12083-020-00993-4","volume":"14","author":"S Khatri","year":"2021","unstructured":"Khatri S, Vachhani H, Shah S, Bhatia J, Chaturvedi M, Tanwar S, Kumar N (2021) Machine learning models and techniques for vanet based traffic management: implementation issues and challenges. Peer-to-Peer Netw Appl 14(3):1778\u20131805","journal-title":"Peer-to-Peer Netw Appl"},{"issue":"1","key":"1241_CR4","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/TITS.2020.3008612","volume":"23","author":"A Haydari","year":"2020","unstructured":"Haydari A, Y\u0131lmaz Y (2020) Deep reinforcement learning for intelligent transportation systems: a survey. IEEE Trans Intell Transport Syst 23(1):11\u201332","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"1241_CR5","doi-asserted-by":"crossref","unstructured":"Chen M-Y, Chiang H-S, Yang K-J (2022) Constructing cooperative intelligent transport systems for travel time prediction with deep learning approaches. IEEE Trans Intell Transport Syst","DOI":"10.1109\/TITS.2022.3148269"},{"key":"1241_CR6","doi-asserted-by":"publisher","first-page":"64420","DOI":"10.1109\/ACCESS.2022.3183642","volume":"10","author":"MN Al-Suqri","year":"2022","unstructured":"Al-Suqri MN, Gillani M (2022) A comparative analysis of information and artificial intelligence toward national security. IEEE Access 10:64420\u201364434","journal-title":"IEEE Access"},{"issue":"1","key":"1241_CR7","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/JSYST.2021.3066776","volume":"16","author":"S Chavhan","year":"2021","unstructured":"Chavhan S, Gupta D, Nagaraju C, Rammohan A, Khanna A, Rodrigues JJ (2021) An efficient context-aware vehicle incidents route service management for intelligent transport system. IEEE Syst J 16(1):487\u2013498","journal-title":"IEEE Syst J"},{"key":"1241_CR8","unstructured":"Statista (2022) Number of registered passenger cars in the republic of ireland from 2010 to 2019. https:\/\/www.statista.com\/statistics\/452305\/ireland-number-of-registered-passenger-cars\/. Accessed 31 Nov 2022"},{"key":"1241_CR9","unstructured":"Central Statistics Office, Ireland (2022) Environmental indicators Ireland 2018, transport. https:\/\/www.cso.ie\/en\/releasesandpublications\/ep\/p-eii\/eii18\/transport\/. Accessed 31 Nov 2022"},{"key":"1241_CR10","unstructured":"Independent.ie (2016) Number of private cars on our roads hits two million. https:\/\/www.independent.ie\/life\/motoring\/car-news\/number-of-private-cars-on-our-roads-hits-two-million-34460268.html. Accessed 31 Nov 2022"},{"issue":"4","key":"1241_CR11","doi-asserted-by":"publisher","DOI":"10.1002\/ett.4427","volume":"33","author":"T Yuan","year":"2022","unstructured":"Yuan T, da Rocha Neto W, Rothenberg CE, Obraczka K, Barakat C, Turletti T (2022) Machine learning for next-generation intelligent transportation systems: a survey. Trans Emerg Telecommun Technol 33(4):e4427","journal-title":"Trans Emerg Telecommun Technol"},{"key":"1241_CR12","doi-asserted-by":"crossref","unstructured":"Hlaing SS, Tin MM, Khin MM, Wai PP, Sinha G (2020) Big traffic data analytics for smart urban intelligent traffic system using machine learning techniques. In: 2020 IEEE 9th global conference on consumer electronics (GCCE), IEEE, pp\u00a0299\u2013300","DOI":"10.1109\/GCCE50665.2020.9291790"},{"issue":"2","key":"1241_CR13","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s42421-020-00020-1","volume":"2","author":"AK Haghighat","year":"2020","unstructured":"Haghighat AK, Ravichandra-Mouli V, Chakraborty P, Esfandiari Y, Arabi S, Sharma A (2020) Applications of deep learning in intelligent transportation systems. J Big Data Anal Transport 2(2):115\u2013145","journal-title":"J Big Data Anal Transport"},{"key":"1241_CR14","unstructured":"Yuan T, da\u00a0Rocha\u00a0Neto WB, Rothenberg C, Obraczka K, Barakat C, Turletti T (2019)\u201cHarnessing machine learning for next-generation intelligent transportation systems: a survey. In: Proceedings of the computational intelligence, communication systems and networks (CICSyN)"},{"key":"1241_CR15","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.websem.2014.07.002","volume":"27","author":"F L\u00e9cu\u00e9","year":"2014","unstructured":"L\u00e9cu\u00e9 F, Tallevi-Diotallevi S, Hayes J, Tucker R, Bicer V, Sbodio M, Tommasi P (2014) Smart traffic analytics in the semantic web with star-city: scenarios, system and lessons learned in Dublin city. J Web Semant 27:26\u201333","journal-title":"J Web Semant"},{"issue":"9","key":"1241_CR16","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1049\/iet-its.2016.0257","volume":"11","author":"Y Jia","year":"2017","unstructured":"Jia Y, Wu J, Ben-Akiva M, Seshadri R, Du Y (2017) Rainfall-integrated traffic speed prediction using deep learning method. IET Intell Transport Syst 11(9):531\u2013536","journal-title":"IET Intell Transport Syst"},{"key":"1241_CR17","doi-asserted-by":"crossref","unstructured":"Dusparic I, Monteil J, Cahill V (2016) Towards autonomic urban traffic control with collaborative multi-policy reinforcement learning. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), IEEE, pp 2065\u20132070","DOI":"10.1109\/ITSC.2016.7795890"},{"key":"1241_CR18","doi-asserted-by":"crossref","unstructured":"Taparia A, Brady M (2021) Bus journey and arrival time prediction based on archived avl\/gps data using machine learning. In: 2021 7th international conference on models and technologies for intelligent transportation systems (MT-ITS), IEEE, pp\u00a01\u20136","DOI":"10.1109\/MT-ITS49943.2021.9529328"},{"issue":"2","key":"1241_CR19","first-page":"873","volume":"9","author":"AO Philip","year":"2018","unstructured":"Philip AO, Saravanaguru R (2018) A vision of connected and intelligent transportation systems. Int J Civ Eng Technol 9(2):873\u2013882","journal-title":"Int J Civ Eng Technol"},{"issue":"9","key":"1241_CR20","doi-asserted-by":"publisher","DOI":"10.1002\/dac.4814","volume":"34","author":"M Nama","year":"2021","unstructured":"Nama M, Nath A, Bechra N, Bhatia J, Tanwar S, Chaturvedi M, Sadoun B (2021) Machine learning-based traffic scheduling techniques for intelligent transportation system: opportunities and challenges. Int J Commun Syst 34(9):e4814","journal-title":"Int J Commun Syst"},{"issue":"24","key":"1241_CR21","doi-asserted-by":"publisher","first-page":"4673","DOI":"10.3390\/math10244673","volume":"10","author":"J Lansky","year":"2022","unstructured":"Lansky J, Rahmani AM, Hosseinzadeh M (2022) Reinforcement learning-based routing protocols in vehicular ad hoc networks for intelligent transport system (its): a survey. Mathematics 10(24):4673","journal-title":"Mathematics"},{"issue":"9","key":"1241_CR22","doi-asserted-by":"publisher","first-page":"4759","DOI":"10.3390\/app12094759","volume":"12","author":"F Alonso","year":"2022","unstructured":"Alonso F, Faus M, Tormo MT, Useche SA (2022) Could technology and intelligent transport systems help improve mobility in an emerging country? Challenges, opportunities, gaps and other evidence from the Caribbean. Appl Sci 12(9):4759","journal-title":"Appl Sci"},{"key":"1241_CR23","unstructured":"Cre\u00df C, Knoll AC (2021) Intelligent transportation systems with the use of external infrastructure: a literature survey. arXiv: 2112.05615"},{"key":"1241_CR24","doi-asserted-by":"crossref","unstructured":"Njoku JN, Nwakanma CI, Amaizu GC, Kim D-S (2022) Prospects and challenges of metaverse application in data-driven intelligent transportation systems. IET Intell Transport Syst","DOI":"10.1049\/itr2.12252"},{"key":"1241_CR25","doi-asserted-by":"crossref","unstructured":"Gillani M, Niaz HA, Farooq MU, Ullah A (2022) Data collection protocols for vanets: a survey. Complex Intell Syst 1\u201330","DOI":"10.1007\/s40747-021-00629-x"},{"key":"1241_CR26","doi-asserted-by":"publisher","first-page":"23438","DOI":"10.1109\/ACCESS.2022.3154780","volume":"10","author":"M Gillani","year":"2022","unstructured":"Gillani M, Niaz HA, Ullah A, Farooq MU, Rehman S (2022) Traffic aware data gathering protocol for vanets. IEEE Access 10:23438\u201323449","journal-title":"IEEE Access"},{"issue":"1","key":"1241_CR27","doi-asserted-by":"publisher","first-page":"8395","DOI":"10.1149\/10701.8395ecst","volume":"107","author":"I Seth","year":"2022","unstructured":"Seth I, Guleria K, Panda SN (2022) Introducing intelligence in vehicular ad hoc networks using machine learning algorithms. ECS Trans 107(1):8395","journal-title":"ECS Trans"},{"key":"1241_CR28","doi-asserted-by":"crossref","unstructured":"Chaymae T, Elkhatir H, Otman A (2022) Recent advances in machine learning and deep learning in vehicular ad-hoc networks: a comparative study. In: International conference on electrical systems & automation. Springer, pp 1\u201314","DOI":"10.1007\/978-981-19-0039-6_1"},{"key":"1241_CR29","doi-asserted-by":"crossref","unstructured":"Gillani M, Ullah A, Niaz HA (2018) Trust management schemes for secure routing in vanets\u2014a survey. In: 2018 12th international conference on mathematics, actuarial science, computer science and statistics (MACS), IEEE, pp\u00a01\u20136","DOI":"10.1109\/MACS.2018.8628440"},{"key":"1241_CR30","doi-asserted-by":"publisher","first-page":"51258","DOI":"10.1109\/ACCESS.2021.3069770","volume":"9","author":"SA Kashinath","year":"2021","unstructured":"Kashinath SA, Mostafa SA, Mustapha A, Mahdin H, Lim D, Mahmoud MA, Mohammed MA, Al-Rimy BAS, Fudzee MFM, Yang TJ (2021) Review of data fusion methods for real-time and multi-sensor traffic flow analysis. IEEE Access 9:51258\u201351276","journal-title":"IEEE Access"},{"issue":"2","key":"1241_CR31","first-page":"631","volume":"65","author":"MK Pandey","year":"2022","unstructured":"Pandey MK (2022) Advance automated highway systems and their impact on intelligent transport systems. J East China Univ Sci Technol 65(2):631\u2013640","journal-title":"J East China Univ Sci Technol"},{"key":"1241_CR32","doi-asserted-by":"publisher","first-page":"27552","DOI":"10.1109\/ACCESS.2021.3058388","volume":"9","author":"RA Nazib","year":"2021","unstructured":"Nazib RA, Moh S (2021) Reinforcement learning-based routing protocols for vehicular ad hoc networks: a comparative survey. IEEE Access 9:27552\u201327587","journal-title":"IEEE Access"},{"issue":"1","key":"1241_CR33","doi-asserted-by":"publisher","first-page":"15","DOI":"10.53375\/ijecer.2021.24","volume":"1","author":"M Gillani","year":"2021","unstructured":"Gillani M, Niaz HA, Tayyab M (2021) Role of machine learning in wsn and vanets. Int J Electr Comput Eng Res 1(1):15\u201320","journal-title":"Int J Electr Comput Eng Res"},{"key":"1241_CR34","doi-asserted-by":"publisher","first-page":"74318","DOI":"10.1109\/ACCESS.2022.3190964","volume":"10","author":"K Kandali","year":"2022","unstructured":"Kandali K, Bennis L, El Bannay O, Bennis H (2022) An intelligent machine learning based routing scheme for vanet. IEEE Access 10:74318\u201374333","journal-title":"IEEE Access"},{"key":"1241_CR35","doi-asserted-by":"crossref","unstructured":"Sataraddi MJ, Kakkasageri MS (2021) Machine learning based vehicle-to-infrastructure communication in vanets. In: 2021 IEEE 18th India council international conference (INDICON), IEEE, pp\u00a01\u20136","DOI":"10.1109\/INDICON52576.2021.9691730"},{"key":"1241_CR36","doi-asserted-by":"crossref","unstructured":"Devi A, Kait R, Ranga V (2022) Automated cluster head selection in fog-vanet via machine learning. In: Communication and intelligent systems. Springer, pp\u00a01169\u20131179","DOI":"10.1007\/978-981-19-2130-8_89"},{"key":"1241_CR37","doi-asserted-by":"crossref","unstructured":"Nayak RP, Sethi S, Bhoi SK, Sahoo KS, Nayyar A (2022) Ml-mds: machine learning based misbehavior detection system for cognitive software-defined multimedia vanets (csdmv) in smart cities. Multim Tools Appl 1\u201321","DOI":"10.1007\/s11042-022-13440-8"},{"key":"1241_CR38","doi-asserted-by":"crossref","unstructured":"Shen L, Tao H, Ni Y, Wang Y, Vladimir S (2023) Improved yolov3 model with feature map cropping for multi-scale road object detection. Meas Sci Technol","DOI":"10.1088\/1361-6501\/acb075"},{"issue":"11","key":"1241_CR39","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ac8368","volume":"33","author":"H Tao","year":"2022","unstructured":"Tao H, Cheng L, Qiu J, Stojanovic V (2022) Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic. Meas Sci Technol 33(11):115005","journal-title":"Meas Sci Technol"},{"issue":"9","key":"1241_CR40","doi-asserted-by":"publisher","first-page":"3448","DOI":"10.3390\/s22093448","volume":"22","author":"AK Kazi","year":"2022","unstructured":"Kazi AK, Khan SM, Farooq U, Hina S (2022) Compacted area with effective links (cael) for data dissemination in vanets. Sensors 22(9):3448","journal-title":"Sensors"},{"issue":"3","key":"1241_CR41","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1002\/acs.3546","volume":"37","author":"P Sun","year":"2023","unstructured":"Sun P, Song X, Song S, Stojanovic V (2023) Composite adaptive finite-time fuzzy control for switched nonlinear systems with preassigned performance. Int J Adapt Control Signal Process 37(3):771\u2013789","journal-title":"Int J Adapt Control Signal Process"},{"issue":"18","key":"1241_CR42","doi-asserted-by":"publisher","first-page":"10139","DOI":"10.1002\/rnc.6354","volume":"32","author":"C Zhou","year":"2022","unstructured":"Zhou C, Tao H, Chen Y, Stojanovic V, Paszke W (2022) Robust point-to-point iterative learning control for constrained systems: a minimum energy approach. Int J Robust Nonlinear Control 32(18):10139\u201310161","journal-title":"Int J Robust Nonlinear Control"},{"key":"1241_CR43","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.matpr.2021.03.479","volume":"47","author":"A Mohandu","year":"2021","unstructured":"Mohandu A, Kubendiran M (2021) Survey on big data techniques in intelligent transportation system (its). Mater Today Proc 47:8\u201317","journal-title":"Mater Today Proc"},{"key":"1241_CR44","doi-asserted-by":"crossref","unstructured":"Balisi AN, Jula H, Chassiakos A (2021) Smart cities: a focus on intelligent transportation systems. In: 2021 IEEE green energy and smart systems conference (IGESSC), IEEE, pp\u00a01\u20137","DOI":"10.1109\/IGESSC53124.2021.9618678"},{"key":"1241_CR45","doi-asserted-by":"crossref","unstructured":"Mulerikkal J, Thandassery S, Rejathalal V, Ayyappan B et\u00a0al (2021) Jp-dap: an intelligent data analytics platform for metro rail transport systems. IEEE Trans Intell Transport Syst","DOI":"10.1109\/TITS.2021.3091542"},{"key":"1241_CR46","doi-asserted-by":"crossref","unstructured":"Nguyen N-L, Vo H-T, Lam G-H, Nguyen T-B, Do T-H (2022) Real-time traffic congestion forecasting using prophet and spark streaming. In: International conference on intelligence of things. Springer, pp\u00a0388\u2013397","DOI":"10.1007\/978-3-031-15063-0_37"},{"key":"1241_CR47","doi-asserted-by":"crossref","unstructured":"Sengul MK, Tarhan C, Tecim V (2022) Application of intelligent transportation system data using big data technologies. In: 2022 innovations in intelligent systems and applications conference (ASYU), IEEE, pp\u00a01\u20136","DOI":"10.1109\/ASYU56188.2022.9925457"},{"issue":"4","key":"1241_CR48","doi-asserted-by":"publisher","first-page":"5075","DOI":"10.1007\/s11227-021-04072-0","volume":"78","author":"H Alazzam","year":"2022","unstructured":"Alazzam H, AbuAlghanam O, Sharieh A (2022) Best path in mountain environment based on parallel a* algorithm and apache spark. J Supercomput 78(4):5075\u20135094","journal-title":"J Supercomput"},{"issue":"2","key":"1241_CR49","doi-asserted-by":"publisher","first-page":"58","DOI":"10.3390\/info13020058","volume":"13","author":"O Azeroual","year":"2022","unstructured":"Azeroual O, Nikiforova A (2022) Apache spark and mllib-based intrusion detection system or how the big data technologies can secure the data. Information 13(2):58","journal-title":"Information"},{"key":"1241_CR50","doi-asserted-by":"crossref","unstructured":"Mohyuddin S, Prehofer C (2021) A scalable data analytics framework for connected vehicles using apache spark. In: 2021 international symposium on electrical, electronics and information engineering, pp 322\u2013329","DOI":"10.1145\/3459104.3459156"},{"issue":"6","key":"1241_CR51","doi-asserted-by":"publisher","first-page":"1990","DOI":"10.1111\/coin.12553","volume":"38","author":"M Jain","year":"2022","unstructured":"Jain M, Vasdev D, Pal K, Sharma V (2022) Systematic literature review on predictive maintenance of vehicles and diagnosis of vehicle\u2019s health using machine learning techniques. Comput Intell 38(6):1990\u20132008","journal-title":"Comput Intell"},{"key":"1241_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2021.103029","volume":"159","author":"E Nagy","year":"2021","unstructured":"Nagy E, Lovas R, Pintye I, Hajnal \u00c1, Kacsuk P (2021) Cloud-agnostic architectures for machine learning based on apache spark. Adv Eng Softw 159:103029","journal-title":"Adv Eng Softw"},{"key":"1241_CR53","doi-asserted-by":"crossref","unstructured":"Ali\u00a0Mohamed M, El-Henawy IM, Salah A (2021) Usages of spark framework with different machine learning algorithms. Comput Intell Neurosci 2021","DOI":"10.1155\/2021\/1896953"},{"issue":"1","key":"1241_CR54","first-page":"1311","volume":"34","author":"A JayaLakshmi","year":"2022","unstructured":"JayaLakshmi A, Kishore KK (2022) Performance evaluation of dnn with other machine learning techniques in a cluster using apache spark and mllib. J King Saud Univ Comput Inf Sci 34(1):1311\u20131319","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"1241_CR55","doi-asserted-by":"crossref","unstructured":"Prajapati GL, Raghuwanshi R (2021) Study of big data analytics tool: Apache spark. In: Big data analytics in cognitive social media and literary texts. Springer, pp\u00a065\u2013100","DOI":"10.1007\/978-981-16-4729-1_4"},{"key":"1241_CR56","doi-asserted-by":"crossref","unstructured":"Kumar K, Sharma NA, Ali AS (2021) Machine learning solutions for investigating streams data using distributed frameworks: literature review. In: 2021 IEEE Asia-Pacific conference on computer science and data engineering (CSDE), pp 1\u20136, IEEE","DOI":"10.1109\/CSDE53843.2021.9718391"},{"key":"1241_CR57","doi-asserted-by":"crossref","unstructured":"Perr-Sauer J, Phillips C, Duran A, Van\u00a0Roijen A(2021) Code artifact for: clustering analysis of commercial vehicles using automatically extracted features from time series data, technical report, National Renewable Energy Lab.(NREL), Golden, CO (United States)","DOI":"10.2172\/1597242"},{"key":"1241_CR58","doi-asserted-by":"crossref","unstructured":"Prathilothamai M, Viswanathan V(2022) Traffic prediction system using iot cluster based evolutionary under sampling approach. Int J Artif Intell Tools 2240024","DOI":"10.1142\/S0218213022400243"},{"key":"1241_CR59","unstructured":"Kozicki TM (2022) The usage of Apache Spark for dynamic open data processing. PhD thesis, Wydzia\u0142 Matematyki i Nauk Informacyjnych"},{"key":"1241_CR60","doi-asserted-by":"crossref","unstructured":"Prehofer C (2021) Challenges of big data and vehicle data. In: 2021 IEEE international conference on autonomic computing and self-organizing systems companion (ACSOS-C), IEEE, pp 287\u2013288","DOI":"10.1109\/ACSOS-C52956.2021.00070"},{"issue":"6","key":"1241_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-021-04556-x","volume":"3","author":"A Shrivastava","year":"2021","unstructured":"Shrivastava A, Verma JPV, Jain S, Garg S (2021) A deep learning based approach for trajectory estimation using geographically clustered data. SN Appl Sci 3(6):1\u201317","journal-title":"SN Appl Sci"},{"key":"1241_CR62","doi-asserted-by":"crossref","unstructured":"Park G-M, Heo YS, Kwon H-Y (2021) Trade-off analysis between parallelism and accuracy of slic on apache spark. In: 2021 IEEE international conference on big data and smart computing (BigComp), IEEE, pp\u00a05\u201312","DOI":"10.1109\/BigComp51126.2021.00011"},{"key":"1241_CR63","doi-asserted-by":"publisher","first-page":"45182","DOI":"10.1109\/ACCESS.2019.2908225","volume":"7","author":"Y Zeng","year":"2019","unstructured":"Zeng Y, Gu H, Wei W, Guo Y (2019) $$Deep-full-range$$: a deep learning based network encrypted traffic classification and intrusion detection framework. IEEE Access 7:45182\u201345190","journal-title":"IEEE Access"},{"key":"1241_CR64","doi-asserted-by":"publisher","first-page":"101842","DOI":"10.1016\/j.adhoc.2019.02.001","volume":"90","author":"M Aloqaily","year":"2019","unstructured":"Aloqaily M, Otoum S, Al Ridhawi I, Jararweh Y (2019) An intrusion detection system for connected vehicles in smart cities. Ad Hoc Netw 90:101842","journal-title":"Ad Hoc Netw"},{"issue":"20","key":"1241_CR65","doi-asserted-by":"publisher","first-page":"4396","DOI":"10.3390\/app9204396","volume":"9","author":"H Liu","year":"2019","unstructured":"Liu H, Lang B (2019) Machine learning and deep learning methods for intrusion detection systems: a survey. Appl Sci 9(20):4396","journal-title":"Appl Sci"},{"key":"1241_CR66","doi-asserted-by":"crossref","unstructured":"Linhares T, Patel A, Barros AL, Fernandez M (2022) Sdntruth: innovative ddos detection scheme for software-defined networks (sdn)","DOI":"10.21203\/rs.3.rs-2223091\/v1"},{"key":"1241_CR67","doi-asserted-by":"crossref","unstructured":"Malliga S, Kogilavani S, Sowmya R (2022) Deep discover: deep learning models for detecting distributed denial of service (ddos) attacks. In: AIP Conference Proceedings, vol 2393, AIP Publishing LLC, p 020191","DOI":"10.1063\/5.0074445"},{"issue":"1","key":"1241_CR68","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3390\/asi5010023","volume":"5","author":"W Jiang","year":"2022","unstructured":"Jiang W, Luo J (2022) Big data for traffic estimation and prediction: a survey of data and tools. Appl Syst Innov 5(1):23","journal-title":"Appl Syst Innov"},{"key":"1241_CR69","doi-asserted-by":"crossref","unstructured":"Gillani M, Ullah A, Niaz HA(2018) Survey of requirement management techniques for safety critical systems. In: 2018 12th international conference on mathematics, actuarial science, computer science and statistics (MACS), IEEE, pp 1\u20135","DOI":"10.1109\/MACS.2018.8628389"},{"key":"1241_CR70","doi-asserted-by":"publisher","first-page":"56158","DOI":"10.1109\/ACCESS.2022.3177659","volume":"10","author":"M Gillani","year":"2022","unstructured":"Gillani M, Niaz HA, Ullah A (2022) Integration of software architecture in requirements elicitation for rapid software development. IEEE Access 10:56158\u201356178","journal-title":"IEEE Access"},{"key":"1241_CR71","doi-asserted-by":"crossref","unstructured":"Gillani M, Niaz HA, Ullah A (2020) Multi-cyclic requirement engineering for educational and industrial models in software development. In: 2020 IEEE 23rd international multitopic conference (INMIC), IEEE, pp 1\u20136","DOI":"10.1109\/INMIC50486.2020.9318148"},{"key":"1241_CR72","doi-asserted-by":"crossref","unstructured":"Ouhssini M, Afdel K, Idhammad M, Agherrabi E (2021) Distributed intrusion detection system in the cloud environment based on apache kafka and apache spark. In: 2021 fifth international conference on intelligent computing in data sciences (ICDS), IEEE, pp 1\u20136","DOI":"10.1109\/ICDS53782.2021.9626721"},{"key":"1241_CR73","unstructured":"Abushwereb M, Alkasassbeh M, Almseidin M, Mustafa M (2022) An accurate iot intrusion detection framework using apache spark. arXiv: 2203.04347"},{"issue":"14","key":"1241_CR74","doi-asserted-by":"publisher","first-page":"7606","DOI":"10.3390\/su13147606","volume":"13","author":"M.\u00a0M Rathore","year":"2021","unstructured":"Rathore M.\u00a0M, Attique\u00a0Shah S, Awad A, Shukla D, Vimal S, Paul A (2021) A cyber-physical system and graph-based approach for transportation management in smart cities. Sustainability 13(14):7606","journal-title":"Sustainability"},{"key":"1241_CR75","unstructured":"The Irish Times (2022) M50 blues: Ireland\u2019s busiest road, dublin\u2019s biggest car park. https:\/\/www.irishtimes.com\/life-and-style\/people\/m50-blues-ireland-s-busiest-road-dublin-s-biggest-car-park-1.3259694. Accessed 31 Aug 2022"},{"key":"1241_CR76","unstructured":"Apache Spark (2022) Spark streaming. https:\/\/spark.apache.org\/. Accessed 25 Dec 2022"},{"issue":"1","key":"1241_CR77","first-page":"1235","volume":"17","author":"X Meng","year":"2016","unstructured":"Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai D, Amde M, Owen S et al (2016) Mllib: machine learning in apache spark. J Mach Learn Res 17(1):1235\u20131241","journal-title":"J Mach Learn Res"},{"issue":"5","key":"1241_CR78","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.12928\/telkomnika.v19i5.21059","volume":"19","author":"AH Ali","year":"2021","unstructured":"Ali AH, Abbod MN, Khaleel MK, Mohammed MA, Sutikno T (2021) Large scale data analysis using mllib. Telkomnika (Telecommunication Computing Electronics and Control) 19(5):1735\u20131746","journal-title":"Telkomnika (Telecommunication Computing Electronics and Control)"},{"key":"1241_CR79","doi-asserted-by":"crossref","unstructured":"Kononenko O, Baysal O, Holmes R, Godfrey MW (2014) Mining modern repositories with elasticsearch. In: Proceedings of the 11th working conference on mining software repositories, pp 328\u2013331","DOI":"10.1145\/2597073.2597091"},{"key":"1241_CR80","unstructured":"Gormley C, Tong Z (2015) Elasticsearch: the definitive guide: a distributed real-time search and analytics engine. O\u2019Reilly Media, Inc"},{"key":"1241_CR81","doi-asserted-by":"crossref","unstructured":"Sharma V (2016) Getting started with kibana. In: Beginning Elastic Stack. Springer, , pp 29\u201344","DOI":"10.1007\/978-1-4842-1694-1_3"},{"key":"1241_CR82","doi-asserted-by":"crossref","unstructured":"Takase W, Nakamura T, Watase Y, Sasaki T (2017) A solution for secure use of kibana and elasticsearch in multi-user environment. arXiv: 1706.10040","DOI":"10.22323\/1.293.0008"},{"key":"1241_CR83","unstructured":"The Society of the Irish Motor Industry (2022) National vehicle statistics. https:\/\/www.simi.ie\/en\/motorstats\/national-vehicle-statistics. Accessed 31 Oct 2022"},{"key":"1241_CR84","unstructured":"Transport Infrastructure Ireland (2022) Irish toll data statistics. https:\/\/www.tii.ie\/roads-tolling\/tolling-information\/tolling-dashboards\/. Accessed 31 Oct 2022"},{"key":"1241_CR85","unstructured":"Transport Infrastructure Ireland (TII) (2022) Transport infrastructure Ireland. https:\/\/www.tii.ie\/. Accessed 25 Dec 2022"},{"key":"1241_CR86","unstructured":"M50 Concession Limited (2022) Live travel times & traffic. https:\/\/www.m50concession.com\/live-travel-times-traffic\/"},{"key":"1241_CR87","unstructured":"Road Safety Division, Department of Transport (2019) Road safety division. https:\/\/www.gov.ie\/en\/organisation-information\/9d873d-road-safety-division\/. Accessed 5 June 2023"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01241-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01241-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01241-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T15:20:28Z","timestamp":1711812028000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01241-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,6]]},"references-count":87,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["1241"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01241-x","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,6]]},"assertion":[{"value":"8 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}