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Smart city article details

Title	Traffic Flow Prediction Based On Improved Lstm And Mobile Big Data In Smart Cities
ID_Doc	58581
Authors	Yao T.; Yang C.
Year	2024
Published	Advances in Transportation Studies, 64
DOI	http://dx.doi.org/10.53136/979122181490322
Abstract	Urban traffic problems are one of the major challenges in the development of smart cities. To effectively alleviate traffic congestion and improve urban traffic management, a distributed big data analytics platform based on the MapReduce computing framework is investigated to process urban traffic data containing millions of traffic flow, speed and timestamp records. The study proposes an improved Long Short-Term Memory (LSTM) neural network algorithm and describes the network structure, training process and parameter tuning in detail. The effectiveness of the improved algorithm was verified through comparative analysis with the traditional ARIMA, linear regression and support vector regression algorithms. The results showed that the improved LSTM algorithm achieved an accuracy of 97.62% in traffic flow prediction, which was significantly better than other algorithms. The results demonstrate that the method can provide reliable technical support and decision-making basis for traffic management in smart cities. © 2024, Aracne Editrice. All rights reserved.
Author Keywords	big data; long short-term memory neural networks; MapReduce; traffic flow prediction

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