Smart City Gnosys

Smart city article details

Title	Data-Driven Urban Mobility - Comprehensive Predictive Modeling For Traffic Congestion
ID_Doc	17495
Authors	Padmaja C.V.R.; Sadasivuni S.T.
Year	2024
Published	Proceedings - 2024 IEEE International Conference on Intelligent Systems, Smart and Green Technologies, ICISSGT 2024
DOI	http://dx.doi.org/10.1109/ICISSGT58904.2024.00037
Abstract	In the rapidly changing landscape of urbanization, this study delves into traffic congestion management within smart cities. Leveraging a diverse dataset from Kaggle, which includes details such as timestamps, weather conditions, road types, and vehicle specifics, the research examines the temporal dynamics of congestion, identifying patterns across various times of the day, week, and year. By applying machine learning models like Linear Regression, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, SVR, K-Neighbors Regressor, and MLP Regressor, the study evaluates their predictive performance, with a focus on distinctions in effectiveness using Mean Squared Error as a metric. The findings highlight the critical role of historical data and weather factors in accurate congestion forecasting, offering valuable insights for the development of smart cities. This research enhances the understanding of urban mobility challenges and provides a practical framework for implementing proactive traffic management strategies in increasingly complex urban environments. © 2024 IEEE.
Author Keywords	Machine Learning; Predictive Modeling; Smart Cities; Traffic Congestion; Urban Mobility

Similar Articles

Id	Similarity	Authors	Title	Published
22862	0.916	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)
934	0.909	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)
37160	0.9	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)
60223	0.9	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)
35078	0.9	Alabi O.O.; Ajagbe S.A.; Kuti O.; Afe O.F.; Ajiboye G.O.; Adigun M.O.	Leveraging Environmental Data For Intelligent Traffic Forecasting In Smart Cities	Communications in Computer and Information Science, 2159 CCIS (2024)
8929	0.894	Mrad S.; Mraihi R.	An Overview Of Model-Driven And Data-Driven Forecasting Methods For Smart Transportation	Studies in Big Data, 132 (2023)
25494	0.891	Almeida A.; Brás S.; Sargento S.; Oliveira I.	Exploring Bus Tracking Data To Characterize Urban Traffic Congestion	Journal of Urban Mobility, 4 (2023)
10797	0.887	Zaman M.; Saha S.; Abdelwahed S.	Assessing The Suitability Of Different Machine Learning Approaches For Smart Traffic Mobility	2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023 (2023)
1927	0.887	Attoui S.-E.; Meddeb M.	A Generic Framework For Forecasting Short-Term Traffic Conditions On Urban Highways	2021 IEEE 8th International Conference on Data Science and Advanced Analytics, DSAA 2021 (2021)
17272	0.887	Mystakidis A.; Geromichalou O.; Tjortjis C.	Data Mining For Smart Cities: Traffic Congestion Prediction	14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023 (2023)