Textual Data in Transportation Research

Techniques and Opportunities

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

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

Transportation is rich in human communication. Travellers often use information services, interact with each other and with transport providers through natural language. And the transport providers and regulators (traffic managers, police, transit operators, etc.) need to constantly communicate to coordinate, plan and inform each other. This generates substantial amounts of structured, semistructured or unstructured data, generated within or outside of an organization's boundary. Such data is often rich in information that becomes absent, or oversimplified e.g., aggregated into dummy variables, in actual research or practical applications. If a researcher could transform such richer data into meaningful variables in her models, new opportunities would arise for e.g., better coping with heterogeneity or random effects. Due to recent advances in computational text analysis, textual data have become utilizable to a much higher degree. The objective of this chapter is to introduce techniques for textual data processing, and provide some recent application examples in transportation research.
Original languageEnglish
Title of host publicationMobility Patterns, Big Data and Transport Analytics : Tools and Applications for Modeling
EditorsConstantinos Antoniou, Loukas Dimitriou, Francisco Pereira
Number of pages25
Place of PublicationAmsterdam
PublisherElsevier
Publication date2019
Pages173-197
Chapter8
ISBN (Print)9780128129708
ISBN (Electronic)9780128129715
DOIs
Publication statusPublished - 2019

Bibliographical note

CBS Library does not have access to the material

Cite this

Kinra, A., Beheshti-Kashi, S., Pereira, F. C., Combes, F., & Rothengatter, W. (2019). Textual Data in Transportation Research: Techniques and Opportunities. In C. Antoniou, L. Dimitriou, & F. Pereira (Eds.), Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling (pp. 173-197). Amsterdam: Elsevier. https://doi.org/10.1016/B978-0-12-812970-8.00008-7
Kinra, Aseem ; Beheshti-Kashi, Samaneh ; Pereira, Francisco Câmara ; Combes, François ; Rothengatter, Werner. / Textual Data in Transportation Research : Techniques and Opportunities. Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling. editor / Constantinos Antoniou ; Loukas Dimitriou ; Francisco Pereira. Amsterdam : Elsevier, 2019. pp. 173-197
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Kinra, A, Beheshti-Kashi, S, Pereira, FC, Combes, F & Rothengatter, W 2019, Textual Data in Transportation Research: Techniques and Opportunities. in C Antoniou, L Dimitriou & F Pereira (eds), Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling. Elsevier, Amsterdam, pp. 173-197. https://doi.org/10.1016/B978-0-12-812970-8.00008-7

Textual Data in Transportation Research : Techniques and Opportunities. / Kinra, Aseem; Beheshti-Kashi, Samaneh; Pereira, Francisco Câmara; Combes, François; Rothengatter, Werner.

Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling. ed. / Constantinos Antoniou; Loukas Dimitriou; Francisco Pereira. Amsterdam : Elsevier, 2019. p. 173-197.

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

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AB - Transportation is rich in human communication. Travellers often use information services, interact with each other and with transport providers through natural language. And the transport providers and regulators (traffic managers, police, transit operators, etc.) need to constantly communicate to coordinate, plan and inform each other. This generates substantial amounts of structured, semistructured or unstructured data, generated within or outside of an organization's boundary. Such data is often rich in information that becomes absent, or oversimplified e.g., aggregated into dummy variables, in actual research or practical applications. If a researcher could transform such richer data into meaningful variables in her models, new opportunities would arise for e.g., better coping with heterogeneity or random effects. Due to recent advances in computational text analysis, textual data have become utilizable to a much higher degree. The objective of this chapter is to introduce techniques for textual data processing, and provide some recent application examples in transportation research.

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Kinra A, Beheshti-Kashi S, Pereira FC, Combes F, Rothengatter W. Textual Data in Transportation Research: Techniques and Opportunities. In Antoniou C, Dimitriou L, Pereira F, editors, Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling. Amsterdam: Elsevier. 2019. p. 173-197 https://doi.org/10.1016/B978-0-12-812970-8.00008-7