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Aug 30, 2023
Social Media-Based Traffic Situational Awareness under Extreme Weather
Authors: Yi Ding [email protected], Hong Tao [email protected], Ruofan Zhang [email protected], Yaxing Cheng [email protected], and Hao Wang [email protected]Author Affiliations
Publication: CICTP 2023
ABSTRACT
Traffic information under extreme weather cannot be collected in a timely and accurate manner by traditional traffic information collection methods, due to various restrictions. Although recent evidence suggests that social media, such as Twitter, is a powerful supplement to traditional traffic information collection methods, which plays an important role in addressing the issue of traffic situational awareness, few studies take Chinese social media data as the object of analysis. To fill this gap, based on the social media data scraped from Weibo, which is the largest Chinese social platform in China, this study provides new insights into comparing the metrics of different machine learning classification algorithms. The results indicate that the long short-term memory (LSTM) classifier outperformed others and achieved 93.8%–95.8% accuracy.
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Published online: Aug 30, 2023
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ASCE Technical Topics:
- Business management
- Data analysis
- Data collection
- Engineering fundamentals
- Engineering profession
- Infrastructure
- Methodology (by type)
- Models (by type)
- Practice and Profession
- Professional development
- Research methods (by type)
- Social factors
- Social network
- Traffic analysis
- Traffic engineering
- Traffic management
- Traffic models
- Traffic safety
- Transportation engineering
Authors
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1School of Transportation, Southeast Univ., Nanjing, China. Email: [email protected]
2School of Transportation, Southeast Univ., Nanjing, China. Email: [email protected]
3School of Transportation, Southeast Univ., Nanjing, China. Email: [email protected]
4School of Transportation, Southeast Univ., Nanjing, China. Email: [email protected]
5Jiangsu Key Laboratory of Urban ITS and School of Transportation, Southeast Univ., Nanjing, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing, China. Email: [email protected]
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ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.