Smart City Gnosys

Smart city article details

Title	Exploring New Traffic Prediction Models To Build An Intelligent Transport System For Smart Cities
ID_Doc	25580
Authors	Mehta V.; Mapp G.; Gandhi V.
Year	2022
Published	Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022
DOI	http://dx.doi.org/10.1109/NOMS54207.2022.9789926
Abstract	The demand for passenger transportation, especially by road, has been increasing due to globalisation, resulting in further delays and traffic congestion. This paper addresses issues to minimise delays and traffic congestion using source and destination information in an urban environment. A journey is defined as the traversal of several road links and junctions. The delays on the links are analysed using M/M/K Markov technique. The delays at a junction are examined using the Zero-Server Markov Chain technique. In order to study multiple junctions, this technique is combined with the Jackson Network model. This combined approach is then used to evaluate the delays at multiple junctions using the Middlesex University VANET Testbed (a real-time vehicular network in London, UK). Initial results indicate that there is more congestion at traffic junctions and hence the Markov Chain analysis will allow better traffic algorithms to reduce congestion at traffic junctions, resulting in reduced journey times as well as less congestion. © 2022 IEEE.
Author Keywords	Jackson Network; Markov Chain Analysis; Traffic Congestion; VANET; Zero Server Markov Chain

Similar Articles

Id	Similarity	Authors	Title	Published
1444	0.869	Vamsi B.; Doppala B.P.; Mahanty M.; Veeraiah D.; Rao J.N.; Rao B.V.S.	A Detailed Case Study On Various Challenges In Vehicular Networks For Smart Traffic Control System Using Machine Learning Algorithms	Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications (2024)
36435	0.868	Sujatha R.; Kuppuswami G.; Nagarajan D.	Markov Chain Long Run Probabilities For Estimation Of Traffic Flow	AIP Conference Proceedings, 2282 (2020)
934	0.866	Bakir D.; Moussaid K.; Chiba Z.; Abghour N.	A Comprehensive Review Of Traffic Congestion Prediction Models: Machine Learning And Statistical Approaches	2024 IEEE International Conference on Computing, ICOCO 2024 (2024)
5762	0.864	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)
18553	0.864	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)
8929	0.863	Mrad S.; Mraihi R.	An Overview Of Model-Driven And Data-Driven Forecasting Methods For Smart Transportation	Studies in Big Data, 132 (2023)
49871	0.862	Saleem M.; Abbas S.; Ghazal T.M.; Adnan Khan M.; Sahawneh N.; Ahmad M.	Smart Cities: Fusion-Based Intelligent Traffic Congestion Control System For Vehicular Networks Using Machine Learning Techniques	Egyptian Informatics Journal, 23, 3 (2022)
28277	0.86	Mohanty A.; Mohapatra A.G.; Mohanty S.K.	Graph-Based Analysis For Optimizing Traffic Flow In Urban Networks	Neural Networks and Graph Models for Traffic and Energy Systems (2025)
30902	0.859	Miri F.; Javadpour A.; Ja'Fari F.; Sangaiah A.K.; Pazzi R.	Improving Resources In Internet Of Vehicles Transportation Systems Using Markov Transition And Tdma Protocol	IEEE Transactions on Intelligent Transportation Systems, 24, 11 (2023)
1530	0.858	Mejdoubi A.; Fouchal H.; Zytoune O.; Ouadou M.	A Distributed Predictive Road Traffic Management System In Urban Vanets	2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019 (2019)