Application Research on Smart City Traffic Management System Based on Deep Learning Algorithm

DOI

10.2991/978-94-6463-453-2_22How to use a DOI?

Keywords

Smart city; Traffic flow prediction; Congestion point identification; Deep learning; Convolutional neural network

Abstract

This study is aimed at addressing key challenges in smart city traffic management systems: traffic flow prediction and congestion point identification, through the development of a deep learning model that integrates Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Graph Neural Networks (GNN). The focus of the research was on collecting and preprocessing a large traffic dataset, including traffic camera images, vehicle movement trajectories, and city traffic network information, to train and validate the proposed model. Key findings demonstrate significant performance improvements of the model in traffic flow prediction and congestion point identification tasks over traditional models like ARIMA, as evidenced by higher accuracy, recall, and F1 scores. Furthermore, through generalization capability testing, this study confirmed the model’s exceptional adaptability, effectively handling traffic data across different cities and time periods. The key conclusion of this research is that deep learning technology can significantly enhance the accuracy and efficiency of smart city traffic management, providing robust data support for urban traffic planning and scheduling. This discovery lays a foundation for further exploration of deep learning applications in the smart city domain.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiaofei Hu
AU  - Lu Yu
AU  - Xiaofang Guo
AU  - Xinting Zhang
PY  - 2024
DA  - 2024/07/26
TI  - Application Research on Smart City Traffic Management System Based on Deep Learning Algorithm
BT  - Proceedings of the 2024 International Conference on Urban Planning and Design (UPD 2024)
PB  - Atlantis Press
SP  - 295
EP  - 302
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-453-2_22
DO  - 10.2991/978-94-6463-453-2_22
ID  - Hu2024
ER  -