Smart City Gnosys

Smart city article details

Title	Machine Learning Based Data Collection Protocol For Intelligent Transport Systems: A Real-Time Implementation On Dublin M50, Ireland
ID_Doc	35927
Authors	Gillani M.; Niaz H.A.
Year	2024
Published	Complex and Intelligent Systems, 10, 2
DOI	http://dx.doi.org/10.1007/s40747-023-01241-x
Abstract	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’s 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’s 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. © The Author(s) 2023.
Author Keywords	Data collection protocol; Data information systems; Energy-efficient communication; Intelligent transport systems; Machine learning; Real-time protocol; Smart cities; VANETs

Similar Articles

Id	Similarity	Authors	Title	Published
5762	0.879	Prakash J.; Murali L.; Manikandan N.; Nagaprasad N.; Ramaswamy K.	A Vehicular Network Based Intelligent Transport System For Smart Cities Using Machine Learning Algorithms	Scientific Reports, 14, 1 (2024)
46591	0.875	Deveshwar P.; Singh T.; Sharma Y.; Bidwe R.V.; Hiremani V.; Devadas R.; Shah K.	Revolutionizing Smart Cities: A Data-Driven Traffic Monitoring System For Real-Time Traffic Density Estimation And Visualization	Lecture Notes in Networks and Systems, 1075 LNNS (2025)
18553	0.874	Lilhore U.K.; Imoize A.L.; Li C.-T.; Simaiya S.; Pani S.K.; Goyal N.; Kumar A.; Lee C.-C.	Design And Implementation Of An Ml And Iot Based Adaptive Traffic-Management System For Smart Cities	Sensors, 22, 8 (2022)
31957	0.872	Guru Vimal Kumar M.; Jude Moses Anto Devakanth J.; Selvapandian D.; Venkatesan R.; Revathi J.; Aruna R.	Integrating Cloud-Based Data Mining Algorithms For Smart City Infrastructure Management And Decision Support Systems	Proceedings - 2024 4th International Conference on Soft Computing for Security Applications, ICSCSA 2024 (2024)
17451	0.87	Gillani M.; Niaz H.A.; Martinez-Pastor B.	Data-Driven Mobility System For Vehicular Communication: A Step Towards Transport Resilience In Intelligent Transport Systems	IEEE Access, 13 (2025)
35056	0.87	Mahmud S.; Day C.M.	Leveraging Data-Driven Traffic Management In Smart Cities: Datasets For Highway Traffic Monitoring	The Rise of Smart Cities: Advanced Structural Sensing and Monitoring Systems (2022)
60223	0.869	Tsalikidis N.; Mystakidis A.; Koukaras P.; Ivaškevičius M.; Morkūnaitė L.; Ioannidis D.; Fokaides P.A.; Tjortjis C.; Tzovaras D.	Urban Traffic Congestion Prediction: A Multi-Step Approach Utilizing Sensor Data And Weather Information	Smart Cities, 7, 1 (2024)
30638	0.868	Sabeer S.; Ali S.S.; Siddiqua A.; Anjum A.	Implementing Ml And Iot-Based Predictive Traffic-Management Systems For Smart Cities	2024 2nd International Conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024 (2024)
37160	0.868	Ei Leen M.W.; Jafry N.H.A.; Salleh N.M.; Hwang H.J.; Jalil N.A.	Mitigating Traffic Congestion In Smart And Sustainable Cities Using Machine Learning: A Review	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13957 LNCS (2023)
35967	0.867	Yuan T.; Da Rocha Neto W.; Rothenberg C.E.; Obraczka K.; Barakat C.; Turletti T.	Machine Learning For Next-Generation Intelligent Transportation Systems: A Survey	Transactions on Emerging Telecommunications Technologies, 33, 4 (2022)