Mining Public Opinion on Transportation Systems Based on Social Media Data

Dawei Li, Yujia Zhang and Cheng Li
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Dawei Li: Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 210096, China
Yujia Zhang: Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 210096, China
Cheng Li: China Academy of Transportation Sciences, No.240, Huixinli, Chaoyang District, Beijing 100029, China

Sustainability, 2019, vol. 11, issue 15, 1-15

Abstract: Public participation plays an important role of traffic planning and management, but it is a great challenge to collect and analyze public opinions for traffic problems on a large scale under traditional methods. Traffic management departments should appropriately adopt public opinions in order to formulate scientific and reasonable regulations and policies. At present, while increasing degree of public participation, data collection and processing should be accelerated to make up for the shortcomings of traditional planning. This paper focuses on text analysis using large data with temporal and spatial attributes of social network platform. Web crawler technology is used to obtain traffic-related text in mainstream social platforms. After basic treatment, the emotional tendency of the text is analyzed. Then, based on the probabilistic topic modeling (latent Dirichlet allocation model), the main opinions of the public are extracted, and the spatial and temporal characteristics of the data are summarized. Taking Nanjing Metro as an example, the existing problems are summarized from the public opinions and improvement measures are put forward, which proves the feasibility of providing technical support for public participation in public transport with social media big data.

Keywords: traffic planning; public participation; big data; content analysis; spatiotemporal properties (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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