Social Media Mining for Understanding Traffic Safety Culture in Washington State Using Twitter Data

Sujon, Mohhammad; Dai, Fei

doi:10.1061/(asce)cp.1943-5487.0000943

Cited by 26 publications

(16 citation statements)

References 50 publications

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“…Sometimes also considered as anomaly detection, event detection has been applied to find unusual behaviors in data, such as sudden rises in sales number, stock price, or tweet frequency in a geographical area. By detecting events, one can correlate anomaly in tweets with that in, for example, stock price [ 99 ] or traffic flow [ 88 ], which cannot be easily captured with regression and classification. Long established in time series analysis [ 31 ], a typical solution is to measure quantities changes at each time step and use a threshold to output a suggestion [ 100 ].…”

Section: An Analysis Of Methodology In Recent Workmentioning

confidence: 99%

Twitter-aided decision making: a review of recent developments

et al. 2022

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Twitter is one of the largest online platforms where people exchange information. In the first few years since its emergence, researchers have been exploring ways to use Twitter data in various decision making scenarios, and have shown promising results. In this review, we examine 28 newer papers published in last five years (since 2016) that continued to advance Twitter-aided decision making. The application scenarios we cover include product sales prediction, stock selection, crime prevention, epidemic tracking, and traffic monitoring. We first discuss the findings presented in these papers, that is how much decision making performance has been improved with the help of Twitter data. Then we offer a methodological analysis that considers four aspects of methods used in these papers, including problem formulation, solution, Twitter feature, and information transformation. This methodological analysis aims to enable researchers and decision makers to see the applicability of Twitter-aided methods in different application domains or platforms.

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Section: An Analysis Of Methodology In Recent Workmentioning

confidence: 99%

Twitter-aided decision making: a review of recent developments

et al. 2022

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“…Many studies on big data involving social media as a source of information to identify disasters [1] location of ambulance services [2] inform about road congestion conditions [3] [4] [5] [6]. Social media also provides public APIs such as Twitter which are used to analyze transportation conditions in an area [4].…”

Section: Related Workmentioning

confidence: 99%

Personalized Route Recommendation Method for Field Survey Officers using Social Media Information and Administrative Border Maps

Hardiyanto¹

2022

icdsos

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Changes in business processes in pandemic conditions are a must. The field survey were most affected, not only the interview process but also the route selection to survey location. To support the field survey officer, it is necessary to provide alternative route choices to the survey location as fast as possible. This research proposed a methodology that combines three information source, administrative border maps, google maps services, and information from social media that elaborated to provide best recommendation route to the assigned survey location. The combination of three different sources can enhance the current existing route that only relies on google map services. Our mechanism was tested on custom my maps application provided by Google and evaluated using system usability scale. This research aims to give the personalized route to field survey officers based on the assigned survey location and information from social media. The limitation of this research is that the social media channels used are still few, in the future, this research can be leveraged by integrating other platforms owned by the government and other public services to enrich the information.

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“…On the other hand, a case study of Washington DC to model the traffic phenomenon was presented in [ 19 ]. This approach consists of establishing keywords, considering “traffic safety” data, which were obtained from Twitter for the last four years.…”

Section: Related Workmentioning

confidence: 99%

Traffic Congestion Analysis Based on a Web-GIS and Data Mining of Traffic Events from Twitter

Salazar-Carrillo

Torres-Ruiz

Davis

et al. 2021

Sensors

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Smart cities are characterized by the use of massive information and digital communication technologies as well as sensor networks where the Internet and smart data are the core. This paper proposes a methodology to geocode traffic-related events that are collected from Twitter and how to use geocoded information to gather a training dataset, apply a Support Vector Machine method, and build a prediction model. This model produces spatiotemporal information regarding traffic congestions with a spatiotemporal analysis. Furthermore, a spatial distribution represented by heat maps is proposed to describe the traffic behavior of specific and sensed areas of Mexico City in a Web-GIS application. This work demonstrates that social media are a good alternative that can be leveraged to gather collaboratively Volunteered Geographic Information for sensing the dynamic of a city in which citizens act as sensors.

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Social Media Mining for Understanding Traffic Safety Culture in Washington State Using Twitter Data

Cited by 26 publications

References 50 publications

Twitter-aided decision making: a review of recent developments

Twitter-aided decision making: a review of recent developments

Personalized Route Recommendation Method for Field Survey Officers using Social Media Information and Administrative Border Maps

Traffic Congestion Analysis Based on a Web-GIS and Data Mining of Traffic Events from Twitter

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