Smart City Gnosys

Smart city article details

Title	Optimizing City Transit: Iot And Gradient Boosting Algorithms For Accurate Bus Arrival Predictions
ID_Doc	40771
Authors	Shanthi S.; Maruthu Kannan B.; Giriprasad S.; Babuji R.; Sivakumar S.; Malathi N.
Year	2024
Published	Proceedings of the 2nd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024
DOI	http://dx.doi.org/10.1109/IITCEE59897.2024.10467430
Abstract	Sustainable cities and people's well-being depend on efficient and reliable public transportation networks. To improve the reliability of bus arrival forecasts, we offer a new method that uses Internet of Things (IoT) data and advanced Gradient Boosting Machine learning algorithms to combine the two. Using real-time information from IoT sensors installed on buses and at strategic nodes along their routes, it constructs a comprehensive dataset that accounts for many variables contributing to bus transit delays, such as traffic, weather, and passenger load. Then, it creates superior prediction models than conventional approaches. It uses Gradient Boosting, an effective ensemble learning methodology. The findings will lead to a more dependable and efficient public transportation system by improving the accuracy of predicted bus arrival times. It helps in the continuous struggle over urban problems with mobility and sets the path for more data-driven, efficient improvement of public transportation. © 2024 IEEE.
Author Keywords	Bus Arrival Predictions; Public Transit Efficiency; Public Transportation; Real-time Data Smart Cities

Similar Articles

Id	Similarity	Authors	Title	Published
32177	0.911	Bhalodiya D.; Sarda J.; Garg D.; Yang T.; Rathore R.S.	Integration Of Iot And Machine Learning Models For Enhancing Efficiency In Smart Public Transportation Systems	Lecture Notes in Networks and Systems, 1293 (2025)
60223	0.884	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)
1591	0.884	Ashwini B.P.; Sumathi R.; Sudhira H.S.	A Dynamic Model For Bus Arrival Time Estimation Based On Spatial Patterns Using Machine Learning	International Journal of Engineering Trends and Technology, 70, 9 (2022)
4110	0.875	Mukhopadhyay S.; Kumar A.; Gupta J.; Bhatnagar A.; Kantipudi M.P.; Singh M.	A Review And Analysis Of Iot Enabled Smart Transportation Using Machine Learning Techniques	International Journal of Transport Development and Integration, 8, 1 (2024)
2953	0.873	Munjal, R; Liu, W; Li, XJ; Gutierrez, J	A Neural Network-Based Sustainable Data Dissemination Through Public Transportation For Smart Cities	SUSTAINABILITY, 12, 24 (2020)
33099	0.872	Adelantado F.; Ammouriova M.; Herrera E.; Juan A.A.; Shinde S.S.; Tarchi D.	Internet Of Vehicles And Real-Time Optimization Algorithms: Concepts For Vehicle Networking In Smart Cities	Vehicles, 4, 4 (2022)
4171	0.869	Zantalis F.; Koulouras G.; Karabetsos S.; Kandris D.	A Review Of Machine Learning And Iot In Smart Transportation	Future Internet, 11, 4 (2019)
32662	0.868	Chen G.; Zhang J.W.	Intelligent Transportation Systems: Machine Learning Approaches For Urban Mobility In Smart Cities	Sustainable Cities and Society, 107 (2024)
18221	0.867	Saravana Kumar E.; Shetty K.; Saravana K.; Vinugna M.K.; Naidu A.S.	Delineation Of Connected Buses And Smart Bus Shelter By Employing Iot And Machine Learning	Proceedings of the 4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020 (2020)
3551	0.867	Sharma V.; Srivastava D.; Kumar L.; Payal M.	A Novel Study On Iot And Machine Learning-Based Transportation	Machine Learning Techniques and Industry Applications (2024)