Smart City Gnosys

Smart city article details

Title	An Ensemble Model For Short-Term Traffic Prediction In Smart City Transportation System
ID_Doc	8075
Authors	Zheng G.; Chai W.K.; Katos V.
Year	2019
Published	Proceedings - IEEE Global Communications Conference, GLOBECOM
DOI	http://dx.doi.org/10.1109/GLOBECOM38437.2019.9014061
Abstract	Smart city visions aim to offer citizens with intelligent services in various aspects of life. The services envisioned have been significantly enhanced with the proliferation of Internet-of-Things (IoT) technology offering real-time and ubiquitous monitoring capability. In this paper, we focus on the short-term traffic flow prediction problem based on real-world traffic data as one critical component of a smart city. In contrast to long-term traffic prediction, accurate prediction of short-term traffic flow facilitates timely traffic management and rapid response. We develop and study a novel ensemble model (EM) based on long short term memory (LSTM), deep autoencoder (DAE) and convolutional neural network (CNN) models. Our approach takes into account both temporal and spatial characteristics of the traffic conditions. We evaluate our proposal against well-known existing prediction models. We use two real traffic data (California and London roadways) with different characteristics to train and test the models. Our results indicate that our proposed ensemble model achieves the most accurate predictions (approximate to 97.50% and approximate to 94.86% accuracy) and is robust against high variance traffic flow.
Author Keywords	Intelligent transportation system; Internet-of-Things; Short-term traffic prediction; Smart city

Similar Articles

Id	Similarity	Authors	Title	Published
1395	0.946	Tripathi A.N.; Sharma B.	A Deep Review: Techniques, Findings And Limitations Of Traffic Flow Prediction Using Machine Learning	Lecture Notes in Mechanical Engineering (2023)
51592	0.93	Pritha A.; Fathima G.	Smart Traffic Management: A Deep Learning Revolution In Traffic Prediction - A Review	IET Conference Proceedings, 2024, 23 (2024)
48697	0.93	Bilotta S.; Collini E.; Nesi P.; Pantaleo G.	Short-Term Prediction Of City Traffic Flow Via Convolutional Deep Learning	IEEE Access, 10 (2022)
3199	0.927	Li Y.; Liu H.; Li Y.; Cao Z.; Duan Z.	A Novel Adaptive Ensemble Model Framework For Short-Term Traffic Flow Prediction Based On Model Selection And Multi-Objective Optimization	Proceedings of SPIE - The International Society for Optical Engineering, 12058 (2021)
39398	0.922	Peng Z.; Yin L.	Nonlinear Prediction Model Of Vehicle Network Traffic Management Based On The Internet Of Things	Systems and Soft Computing, 7 (2025)
34028	0.922	Miao Z.; Liao Q.	Iot-Based Traffic Prediction For Smart Cities	IEEE Access, 13 (2025)
21773	0.919	Parveen Banu S.; Patil Y.M.; Somasundaram R.; Santhosh C.; Singh D.P.; Manikandan G.	Edge Computing-Based Short-Term Traffic Flow Forecast For The Smart City Employing 5G Internet Vehicles	Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2024 (2024)
61010	0.917	Almeida A.; Brás S.; Oliveira I.; Sargento S.	Vehicular Traffic Flow Prediction Using Deployed Traffic Counters In A City	Future Generation Computer Systems, 128 (2022)
44467	0.917	Saranya V.S.; Subbarao G.; Balakotaiah D.; Bhavsingh M.; Babu K.S.; Dhanikonda S.R.	Real-Time Traffic Flow Optimization Using Adaptive Iot And Data Analytics: A Novel Deepstreamnet Model	4th International Conference on Sustainable Expert Systems, ICSES 2024 - Proceedings (2024)
58567	0.916	Wang Y.	Traffic Flow Forecasting In Smart Cities With Deep Learning	Proceedings of SPIE - The International Society for Optical Engineering, 13421 (2024)