Smart City Gnosys

Smart city article details

Title	Adoption Of Smart Traffic System To Reduce Traffic Congestion In A Smart City
ID_Doc	6459
Authors	Aroba O.J.; Mabuza P.; Mabaso A.; Sibisi P.
Year	2023
Published	Lecture Notes in Networks and Systems, 668 LNNS
DOI	http://dx.doi.org/10.1007/978-3-031-29857-8_82
Abstract	Cities across the world suffer significantly from traffic congestion. Governments are trying to harness the power of today's computing, networking, and communication technologies to build system that can improve the efficiency of current road traffic and conditions. The study investigated the purpose efficiencies of intelligent system to assess their performance. Considering the findings, it can be said that traffic flow forecasting (TFF) possibilities are numerous, involve a variety of technologies, and can significantly reduce most traffic issues in smart cities. The studies were later evaluated to find similarities, content, benefits, and disadvantages of traffic congestion. By applying the project management tools such as the performance metrics and SQERT model were used to evaluate and prioritize the state-of-the-art methods. A classical model was proposed to improve upon and determine the traffic dangers that affect road users and aggregate the information about traffic from vehicles, traffic lights, and roadside sensors. These on-road sensors (ORS) performance are used for analyses such are vehicle classification, speed calculations, and vehicle counts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Congestion; Deep Learning; Forecasting; Scope Quality Effort Risk and Timing (SQERT); Smart City; Traffic System Sensors

Similar Articles

Id	Similarity	Authors	Title	Published
24036	0.9	Kabir Md.H.; Islam Md.S.	Enhancing Traffic Flow And Reducing Congestion: A Smart City Approach With An Iot-Based Intelligent Traffic Management System	2024 International Conference on Innovations in Science, Engineering and Technology: Innovative Technologies for Global Solutions, ICISET 2024 (2024)
19433	0.892	Al-Jawahry H.M.	Developing An Intelligent Traffic Management System For Smart Cities Through The Integration Of Machine Learning And Iot Technologies	Lecture Notes in Networks and Systems, 1306 LNNS (2025)
8929	0.889	Mrad S.; Mraihi R.	An Overview Of Model-Driven And Data-Driven Forecasting Methods For Smart Transportation	Studies in Big Data, 132 (2023)
42891	0.888	Mirza N.M.; Ali A.; Shifa N.; Ishak M.K.; Ammar K.	Predictive Modeling For Smart Traffic Systems: Harnessing Iot Data Insights	2023 24th International Arab Conference on Information Technology, ACIT 2023 (2023)
36219	0.885	Ivanov M.; Danchenko M.; Barabanov A.; Sokolitsyn A.	Manage Traffic Flows Within The City Using Smart City Technologies	ACM International Conference Proceeding Series (2020)
58655	0.883	Shanthi D.L.; Prasanna K.; Desai V.; Agarwal S.; Shetty V.M.M.; Rakesh A.S.	Traffic Prediction System Using Iot In Smart City Perspective	2021 IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2021 (2021)
50549	0.882	Sharma A.; Madan V.; Bhargav V.; Gulati N.	Smart City Traffic Control System: A Literature Review	Proceedings of the 14th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2024 (2024)
22862	0.881	Jenifer J.; Jemima Priyadarsini R.	Empirical Research On Machine Learning Models And Feature Selection For Traffic Congestion Prediction In Smart Cities	International Journal on Recent and Innovation Trends in Computing and Communication, 11 (2023)
44468	0.879	Dmitrieva E.; Pathani A.; Pushkarna G.; Acharya P.; Rana M.; Surekha P.	Real-Time Traffic Management In Smart Cities: Insights From The Traffic Management Simulation And Impact Analysis	BIO Web of Conferences, 86 (2024)
735	0.879	Djahel, S; Doolan, R; Muntean, GM; Murphy, J	A Communications-Oriented Perspective On Traffic Management Systems For Smart Cities: Challenges And Innovative Approaches	IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 17, 1 (2015)