Exploratory Analysis of Unmanned Aircraft Sightings using Text Mining

Das, Subasish

doi:10.1177/0361198120987230

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Text mining is a popular data mining methods. In recent years, many transportation engineering studies adopted different NLP methods in their analytical framework ( 15 – 31 ). It is a branch of NLP that provides exploratory analysis of unstructured datasets.…”

Section: Methodsmentioning

confidence: 99%

News Media Mining to Explore Speed-Crash-Traffic Association During COVID-19

Das

Sarkar

2022

Transportation Research Record: Journal of the Transportation R

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The COVID-19 pandemic has affected the lives of everyone from almost every perspective. Research communities from many domains have contributed to quantifying and mitigating the influence of the ongoing pandemic. The public depend on news media for reliable information. At the start of the pandemic in March 2020, many news media articles started to report on quieter roads resulting from travel restriction and work from home mandates, but also more excessive speeding and more traffic fatalities. Although the news media’s representation of the crash-speed association is often not based on data-driven safety analysis, the extent to which the media place emphasis on such news content has never been adequately quantified. The current study performed content analysis and text mining (both text network analysis and topic modeling) to explore the representations of speed-crash association in news media (local, national, and international) to provide insights into the generated news content. Findings reveal that topics such as a surge in fatal crashes, careless driving, driving under the influence, law enforcement, and equity issues, as well as impacts on pedestrians and bicyclists, were frequently highlighted by local, national, and international news media during the pandemic era.

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Section: Methodsmentioning

confidence: 99%

News Media Mining to Explore Speed-Crash-Traffic Association During COVID-19

Das

Sarkar

2022

Transportation Research Record: Journal of the Transportation R

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“…Text mining methods, a very effective tool in exploring unstructured textual data, can identify trends, insights, and anomalies in complex textual data with limited efforts. NLP tools such as text mining have been widely used in transportation research (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29). Many transportation studies applied different text mining tools to explore insights from unstructured crash narrative data (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42).…”

Section: Crash Narrative Studiesmentioning

confidence: 99%

Topic Models from Crash Narrative Reports of Motorcycle Crash Causation Study

Das¹,

Dutta

Tsapakis³

2021

Transportation Research Record: Journal of the Transportation R

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The Motorcycle Crash Causation Study (MCCS) is a matched case-control study that contains a very wide list of crash contributing factors associated with motorcycle crash occurrences. It contains information such as motorcycle information, rider information, and associated trip information. This study also provides crash narrative information that presents an in-depth narrative discussion of the crash causation. Because of the plethora of information, it is critical to investigate MCCS-related data. Some studies examined the structured information in MCCS datasets. There is no in-depth study that has examined the unstructured textual contents in the MCCS data. This study aims to mitigate this research gap by applying different natural language processing tools (e.g., text mining, topic modeling). Fatal and non-fatal crash narratives are clustered separately to gain insights pertaining to the injury level. The findings of this study will contribute to the ongoing studies on MCCS to better understand the crash causation mechanism associated with motorcycle crashes.

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Twelve-Year Analysis of Transportation Research Board Annual Meeting’s Official Hashtag

Das

Dutta

2021

Transportation Research Record: Journal of the Transportation R

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Twitter, among other social media platforms, has become more popular over time. Social media platforms underpin the way scholars share ideas, propagate the latest emergence of evidence, and adopt new practices, by providing a virtual platform for interacting, socializing, and sharing information from academic conferences with the outside world beyond the physical location. The Transportation Research Board Annual Meeting (TRBAM) is the largest annual conference for transportation engineering and science, and the hashtag for the conference, #TRBAM, was used first in 2009. This paper aims to perform an observational study based on the interactions on Twitter surrounding this hashtag by collecting all original #TRBAM tweets for 12 years (2009–2020). A general trend in the data is that the quantitative measures (tweets, retweets, and favorites) are all much higher during the conference month compared with other months. Top trending topics included: transit, safety, bike or non-motorized mode of transportation, data, and freight. Overall, the communication pattern shows more dispersion than the central tendency. The findings of this study highlight the need to implement and improve strategies to help transportation research communities encourage continuous and active participation during and after conferences. More active engagement among attendees will help maintain the momentum of information sharing and expand the traffic safety platform globally.

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Exploratory Analysis of Unmanned Aircraft Sightings using Text Mining

Cited by 5 publications

References 18 publications

News Media Mining to Explore Speed-Crash-Traffic Association During COVID-19

News Media Mining to Explore Speed-Crash-Traffic Association During COVID-19

Topic Models from Crash Narrative Reports of Motorcycle Crash Causation Study

Twelve-Year Analysis of Transportation Research Board Annual Meeting’s Official Hashtag

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