Public Transportation Analysis Based on Social Media Data

Public participation has become an important part of traffic planning and policy-making, but due to the limits of project cycle and cost, collecting and processing public opinions on a large scale seems to be a huge challenge. Relevant government departments should accept public opinions and make scientific and reasonable policies. This study focuses on how to use big data from social network platforms for content analysis. Data about public opinions are collected from the internet by means of a web crawler. A latent Dirichlet allocation (LDA) topic model is built to summarize public opinions and traffic problems. This paper also uses the Nanjing subway system as an example to introduce the whole procedure of content analysis and sum up the spatiotemporal properties of data. According to the results, some corresponding measures for the Nanjing metro system are proposed to improve the operation and management.