Textual Data in Transportation Research: Techniques and Opportunities-Reference-Cited by-同舟云学术

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Textual Data in Transportation Research: Techniques and Opportunities

Published:2019 Issue: Volume: Page:173-197
ISSN:
Container-title:Mobility Patterns, Big Data and Transport Analytics
language:
Short-container-title:

Author:

Kinra Aseem,Kashi Samaneh Beheshti,Pereira Francisco Câmara,Combes François,Rothengatter Werner

Publisher

Elsevier

Reference47 articles.

1. Natural Language Processing with Python;Bird,2009

2. Probabilistic topic models;Blei;Commun. ACM,2012

3. Latent dirichlet allocation;Blei;J. Mach. Learn. Res.,2003

4. Big data analytics: a case study of public opinion towards the adoption of driverless cars;Buch,2018

5. Mapping social media for transportation studies;Chaniotakis;IEEE Intell. Syst.,2016

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1. Social Media Analytics in Operations and Supply Chain Management: Opportunities, Challenges, and Paradoxes;Advanced Studies in Diginomics and Digitalization;2022

2. Automatic topics extraction from crowdsourced cyclists near-miss and collision reports using text mining and Artificial Neural Networks;International Journal of Transportation Science and Technology;2021-10

3. Door-to-door air travel: Exploring trends in corporate reports using text classification models;Technological Forecasting and Social Change;2021-09

4. Discovering latent themes in traffic fatal crash narratives using text mining analytics and network topology;Accident Analysis & Prevention;2021-02

5. Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark;Transport Policy;2020-11

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