A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data
This work addresses traffic management by providing a more efficient classification method, but it is incremental as it applies an existing deep learning model to a new data source.
The study tackled traffic flow state classification using smartphone sensor data, achieving superior classification performance and computational efficiency compared to traditional machine learning methods with a dataset of 747,856 data points.
This study proposes a Deep Belief Network model to classify traffic flow states. The model is capable of processing massive, high-density, and noise-contaminated data sets generated from smartphone sensors. The statistical features of Vehicle acceleration, angular acceleration, and GPS speed data, recorded by smartphone software, are analyzed, and then used as input for traffic flow state classification. Data from a five-day experiment is used to train and test the proposed model. A total of 747,856 sets of data are generated and used for both traffic flow states classification and sensitivity analysis of input variables. The result shows that the proposed Deep Belief Network model is superior to traditional machine learning methods in both classification performance and computational efficiency.