Mobile Phone Data Feature Denoising for Expressway Traffic State Estimation

Abstract

Due to their wide coverage, low acquisition cost and large data quantity, the mobile phone signaling data are suitable for fine-grained and large-scale estimation of traffic conditions. However, the relatively high level of data noise makes it difficult for the estimation to achieve sufficient accuracy. According to the characteristics of mobile phone data noise, this paper proposed an improved density peak clustering algorithm (DPCA) to filter data noise. In addition, on the basis of the long short-term memory model (LSTM), a traffic state estimation model based on mobile phone feature data was established with the use of denoising data to realize the estimation of the expressway traffic state with high precision, fine granules, and wide coverage. The Shanghai–Nanjing Expressway was used as a case study area for method and model verification, the results of which showed that the denoising method proposed in this paper can effectively filter data noise, reduce the impact of extreme noise data, significantly improve the estimation accuracy of the traffic state, and reflect the actual traffic situation in a fairly satisfactory manner.