Estimating traffic flow states with smart phone sensor data

Abstract

This study proposes a framework to classify traffic flow states. The framework 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 collected by 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 state classification and sensitivity analysis of input variables. After applying various algorithms to the proposed framework, the study found that acceleration and angular acceleration data can increase the accuracy of traffic flow classification significantly. When the hyper-parameters of the Deep Belief Network model are optimized by the Differential Evolution Grey Wolf Optimizer algorithm, the classification accuracy is further improved. The results have demonstrated the effectiveness of using smartphone sensor data to estimate the traffic flow states and shown that our proposed model outperforms some traditional machine learning methods in traffic flow state classification accuracy.