Search Results (1)

To better facilitate government management and planning based on public opinion, it is essential to propose a method for extracting public opinion perception features in consideration of an integrated framework, which aims at industry monitoring and decision-making. Based on fundamental characteristics of ordinary traffic incidents, this paper develops a perception features system of public opinion consisting of four modules, where the construction methods have been elaborated. First, mining thematic features is realized via the similarity calculation of text vector. Second, based on summarized Chinese expression patterns, time extraction rules, and a five-layer tree-like spatial feature thesaurus are established to extract spatiotemporal features. Third, the modeling of the emotional features is achieved by a dictionary-based analysis model. Fourth, the evolutional features are extracted by the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). In view of the attributes of each module, an integrated framework is built to determine the collaboration relationship of feature indicators. Finally, a case study of Shenzhen public transport has been performed to illustrate the application of proposed methods. Results show that the strong odor in electric buses and a rumor that electric buses have great radiation are two main causes of the decrease in passenger satisfaction in the first quarter of 2017. In contrast, adding new bus lines, increasing service frequency, and guaranteeing the bus-lane right will improve passenger satisfaction, which is basically consistent with the official report. It should be noticed that the developed framework has been validated in the case study of passenger satisfaction analysis, while it can be extensively replicated in other fields. Furthermore, it is important for stakeholders to grasp the public perception of transportation services, in order to enhance public participation in transportation management and decision-making. Full article