Smart City Gnosys

Smart city article details

Title	An Integrated Framework For Real-Time Intelligent Traffic Management Of Smart Highways
ID_Doc	8446
Authors	Zhang Q.; Shi Y.; Yin R.; Tao H.; Xu Z.; Wang Z.; Chen S.; Xing J.
Year	2023
Published	Journal of Transportation Engineering Part A: Systems, 149, 7
DOI	http://dx.doi.org/10.1061/JTEPBS.TEENG-7729
Abstract	The new generation smart highways (NGSH) have emerged as irresistible trends to enhance the efficiency and safety of transportation systems. An integral component of the NGSH is the automation of the intelligent traffic management system (ITMS). This study investigates an integrated framework for the ITMS that incorporates the fine-grained microscopic simulation and deep learning technologies based on real-time traffic data. The framework commences by performing dynamic corrections based on the license plate, vehicle speed, location, and other information provided by the real-time bayonet data in order to simulate the realistic traffic flow along the highway. A deep learning model based on long short-term memory (LSTM) is then applied to predict the short-term traffic volume on major highway segments. Based on prediction results, a collaborative management method is constructed that combines variable speed limits and ramp metering. The case study on the Shanghai-Hangzhou-Ningbo Highway in China suggests the real-time simulation model can control the average error of the traffic volume on the main segments by 4.58%. The LSTM-based model can accurately predict the short-term traffic volume with a relative error of 85% below 15% in both offline and online modes. Consequently, the proposed collaborative framework improves the average speed and traffic volume of controlled sections by 3.62% and 4.35%, respectively, demonstrating its effectiveness in improving the operation and management of the smart highways. © 2023 American Society of Civil Engineers.
Author Keywords	Deep learning; Intelligent transportation; Real-time simulation; Smart highway; Traffic management and control

Similar Articles

Id	Similarity	Authors	Title	Published
24037	0.891	Aljebreen M.; Alamro H.; Al-Mutiri F.; Othman K.M.; Alsumayt A.; Alazwari S.; Hamza M.A.; Mohammed G.P.	Enhancing Traffic Flow Prediction In Intelligent Cyber-Physical Systems: A Novel Bi-Lstm-Based Approach With Kalman Filter Integration	IEEE Transactions on Consumer Electronics, 70, 1 (2024)
51592	0.89	Pritha A.; Fathima G.	Smart Traffic Management: A Deep Learning Revolution In Traffic Prediction - A Review	IET Conference Proceedings, 2024, 23 (2024)
8489	0.889	Sheeba G.; Selvaganesan J.	An Intelligent And Resolute Traffic Management System Using Grcnet-Stmo Model For Smart Vehicular Networks	International Journal of Information Technology (Singapore), 16, 8 (2024)
1927	0.884	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)
38701	0.882	Praveen Kumar B.; Hariharan K.	Multivariate Time Series Traffic Forecast With Long Short Term Memory Based Deep Learning Model	Proceedings of 2020 IEEE International Conference on Power, Instrumentation, Control and Computing, PICC 2020 (2020)
58657	0.881	Selvan C.; Senthil Kumar R.; Iwin Thanakumar Joseph S.; Malin Bruntha P.; Amanullah M.; Arulkumar V.	Traffic Prediction Using Gps Based Cloud Data Through Rnn-Lstm-Cnn Models: Addressing Road Congestion, Safety, And Sustainability In Smart Cities	SN Computer Science, 6, 2 (2025)
2156	0.88	Yang S.	A Hybrid Deep Learning Model Combining Multi-Scale Self-Attention And Lstm For Accurate Traffic Flow Prediction	IET Conference Proceedings, 2025, 2 (2025)
32025	0.879	Fatorachian H.; Kazemi H.	Integrating Learning-Based Solutions In Intelligent Transportation Systems: A Conceptual Framework And Case Studies Validation	Cogent Engineering, 11, 1 (2024)
22656	0.878	Rafalia N.; Moumen I.; Raji F.Z.; Abouchabaka J.	Elevating Smart City Mobility Using Rae-Lstm Fusion For Next-Gen Traffic Prediction	Indonesian Journal of Electrical Engineering and Computer Science, 35, 1 (2024)
32617	0.877	J J.S.; G A.K.; kumar E.; Raju K.N.; Sudha V.; Kshirsagar P.R.; Tirth V.; Rajaram A.	Intelligent Traffic Prediction System Using Hybrid Convolutional Neural Networks For Smart Cities	Multimedia Tools and Applications (2024)