Real-Time Detection of Traffic From Twitter Stream Analysis

D’Andrea, Eleonora; Ducange, Pietro; Lazzerini, Beatrice; Marcelloni, Francesco

doi:10.1109/tits.2015.2404431

Cited by 283 publications

(159 citation statements)

References 41 publications

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“…A recent application of fuzzy sets and clustering to guide the selection of rail crossings for active safety systems (e.g., bells, lights, and barriers) is in [1]. Tey et al [2] describe the use of logistic regression and mixed regression to model the behaviour of drivers at railway crossings. …”

Section: IImentioning

confidence: 99%

Analysing the Train Accident Injuries using Mining Techniques

Nireesha¹

2017

IJRASET

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Rail accidents describe a significant safety concern for the transportation industry in multiple countries. To better understand the contributors to these extreme accidents, the Federal Railroad Administration has established the railroads involved in accidents to submit reports that contain both fixed function entries and narratives that explain the characteristics of the accident. While a number of studies have noticed the fixed fields, nothing has done an extensive analysis of the narratives. This project describes the use of mining with a combination of techniques to automatically discover accident characteristics that can confess a better understanding of the contributors to the accidents. The results show that predictive accuracy for accident costs significantly improves on the use of features found by mining and predictive accuracy also improves through the use of modern ensemble methods. Importantly, this study furthermore shows through case examples how the findings from mining of the narratives can improve understanding of the contributors to rail accidents in ways not possible on only fixed field analysis of the accident reports.

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Section: IImentioning

confidence: 99%

Analysing the Train Accident Injuries using Mining Techniques

Nireesha¹

2017

IJRASET

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“…En la literatura existe una variedad de trabajos que se centran en la clasificación de publicaciones de Twitter 1 para la detección de eventos relacionados al tránsito [2], [3], [4], [5]. En general, la clasificación de publicaciones de Twitter es llevada a cabo mediante la utilización de técnicas de Machine Learning.…”

Section: Introductionunclassified

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Vallejos¹,

Caimmi²,

Alonso³

et al. 2018

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Nowadays, social networks have become in a communication medium widely used to disseminate any type of information. In particular, the shared information in social networks usually includes a considerable number of traffic incidents reports of specific cities. In light of this, specialized social networks have emerged for detecting and disseminating traffic incidents, differentiating from generic social networks in which a wide variety of topics are communicated. In this context, Twitter is a case in point of a generic social network in which its users often share information about traffic incidents, while Waze is a social network specialized in traffic. In this paper we present a comparative study between Waze and an intelligent approach that detects traffic incidents by analyzing publications shared in Twitter. The comparative study was carried out considering Ciudad Autónoma de Buenos Aires (CABA), Argentina, as the region of interest. The results of this work suggest that both social networks should be considered as complementary sources of information. This conclusion is based on the fact that the proportion of mutual detections, i.e. traffic incidents detected by both approaches, was considerably low since it did not exceed 6% of the cases. Moreover, the results do not show that any of the approaches tend to anticipate in time to the other one in the detection of traffic incidents.Resumen Hoy en día, las redes sociales se han convertido en un medio de comunicación ampliamente utilizado para divulgar todo tipo de información. En particular, entre la información que es compartida se suelen incluir reportes de incidentes de tránsito de ciudades específicas. En vista de esto, aparte de las redes sociales genéricas en donde se comunican una amplia variedad de temas, han surgido redes sociales especializadas en la detección y divulgación de incidentes de tránsito. En este contexto, Twitter es un ejemplo de red social genérica en donde sus usuarios suelen informar incidentes de tránsito, mientras que Waze es una red social especializada en tránsito. En este artículo presentamos un estudio comparativo entre Waze y un enfoque inteligente que detecta incidentes de tránsito a partir del análisis de publicaciones compartidas en Twitter. El estudio comparativo fue realizado considerando a la Ciudad Autónoma de Buenos Aires (CABA), Argentina, como región de interés. Los resultados de este trabajo sugieren que ambos enfoques deberían ser considerados como fuentes de información complementarias. Esta conclusión se fundamenta en que la proporción de detecciones mutuas, es decir incidentes de transito detectados por ambos enfoques, resultó ser considerablemente baja no superando el 6% de los casos. Además, los resultados no evidencian que alguno de los enfoques tienda a anticipar temporalmente a su similar en la detección de incidentes.

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“…Once the friendship is accepted, then your friend's friend space becomes part of your network. Hence your extended network involves your friend's friends also even though there is no direct connection between you both [4] - [7]. LinkedIn is a professional online social networking site which helps business professionals to find jobs, connect with potential partners.…”

Section: Introductionmentioning

confidence: 99%

An Overview on User Profiling in Online Social Networks

Vasanthakumar¹,

Sunithamma²,

Shenoy³

et al. 2017

IJAIS

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Advances in Online Social Networks is creating huge data day in and out providing lot of opportunities to its users to express their interest and opinion. Due to the popularity and exposure of social networks, many intruders are using this platform for illegal purposes. Identifying such users is challenging and requires digging huge knowledge out of the data being flown in the social media. This work gives an insight to profile users in online social networks. User Profiles are established based on the behavioral patterns, correlations and activities of the user analyzed from the aggregated data using techniques like clustering, behavioral analysis, content analysis and face detection. Depending on application and purpose, the mechanism used in profiling users varies. Further study on other mechanisms used in profiling users is under the scope of future endeavors.

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Real-Time Detection of Traffic From Twitter Stream Analysis

Cited by 283 publications

References 41 publications

Analysing the Train Accident Injuries using Mining Techniques

Analysing the Train Accident Injuries using Mining Techniques

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An Overview on User Profiling in Online Social Networks

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