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Immediate traffic flow monitoring and management based on multimodal data in cloud computing
- 1 Electrical and Computer Engineering,University of Illinois at Urbana-Champaign,Champaign, Illinois,USA
- 2 Computer Science and Technology, China University of Geosciences, Bejing, China
- 3 Computer Science, Northeastern University, CA, USA
- 4 Computer Science, Northeastern University, San Jose, CA, USA
- 5 Information Science, Trine University, Phoenix, AZ, USA
* Author to whom correspondence should be addressed.
Abstract
This study uses cloud computing platform to process multi-modal data, and constructs a traffic flow prediction model based on LSTM neural network by integrating data from multiple dimensions such as traffic flow, occupancy and speed. In the process of model construction, we fully consider the hourly characteristics and hysteresis characteristics, and carry out fine scaling and splitting of the data to improve the accuracy and generalization ability of the model. The experimental results show that our model outperforms the baseline on both the training set and the test set, which verifies its effectiveness in traffic flow prediction. By keeping our models in a cloud environment, we provide reliable tools and support for future real-time data analysis and traffic management decisions. This study provides an important reference for the development of traffic management system based on cloud computing, and also provides new ideas and methods for other fields to solve practical problems by using multi-modal data.
Keywords
Cloud computing,; Multimodal data,; Traffic flow forecast,; LSTM neural network
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Cite this article
Ding,W.;Tan,H.;Zhou,H.;Li,Z.;Fan,C. (2024). Immediate traffic flow monitoring and management based on multimodal data in cloud computing. Applied and Computational Engineering,67,267-272.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
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