Immediate traffic flow monitoring and management based on multimodal data in cloud computing
Weike Ding, Hao Tan, Huiming Zhou, Zihan Li, Chao Fan
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.