Real-time traffic classification with Twitter data mining

Kurniawan, Dwi Aji; Wibirama, Sunu; Setiawan, Noor Akhmad

doi:10.1109/iciteed.2016.7863251

Cited by 31 publications

(29 citation statements)

References 8 publications

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“…Tweets collected using a spatial extent are geotagged whereas tweets collected by keywords or by following some specific user accounts are typically not geotagged. Some researchers [8], [22] used multiple strategies to collect traffic related tweets. For example, Wang and colleagues [8] collected tweets from official accounts, using pre-defined road names and using circular search areas along the road network to collect geotagged tweets near roads.…”

Section: State-of-the-artmentioning

confidence: 99%

“…D'Andrea and others used inverse document frequency (IDF) as features [11]. Similarly, other researchers used a single word tokens (unigram) and multiple word tokens (n-gram) and their associated term frequencies (TF) as feature vectors [7], [8], [12], [14], [15], [22], [24]. Gu and colleagues used only a selected number of unigrams pertaining to traffic incidents in the US [24].…”

Section: State-of-the-artmentioning

confidence: 99%

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Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Das

Purves

2020

IEEE Trans. Intell. Transport. Syst.

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Detecting traffic events and their locations is important for an effective transportation management system and better urban policy making. Traffic events are related to traffic accidents, congestion, parking issues, to name a few. Currently, traffic events are detected through static sensors e.g., CCTV camera, loop detectors. However they have limited spatial coverage and high maintenance cost, especially in developing regions. On the other hand, with Web 2.0 and ubiquitous mobile platforms, people can act as social sensors sharing different traffic events along with their locations. We investigated whether Twitter-a social media platform can be useful to understand urban traffic events from tweets in India. However, such tweets are informal and noisy and containing vernacular geographical information making the location retrieval task challenging. So far most authors have used geotagged tweets to identify traffic events which accounted for only 0.1%-3% or sometimes less than that. Recently Twitter has removed precise geotagging, further decreasing the utility of such approaches. To address these issues, this research explored how ungeotagged tweets could be used to understand traffic events in India. We developed a novel framework that does not only categorize traffic related tweets but also extracts the locations of the traffic events from the tweet content in Greater Mumbai. The results show that an SVM based model performs best detecting traffic related tweets. While extracting location information, a hybrid georeferencing model consists of a supervised learning algorithm and a number of spatial rules outperforms other models. The results suggest people in India, especially in Greater Mumbai often share traffic information along with location mentions, which can be used to complement existing physical transport infrastructure in a cost-effective manner to manage transport services in the urban environment.

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Section: State-of-the-artmentioning

confidence: 99%

Section: State-of-the-artmentioning

confidence: 99%

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Das

Purves

2020

IEEE Trans. Intell. Transport. Syst.

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“…Kurniawan et al [30] collected data consisting of 110,449 tweets for seven days from official traffic accounts from the Indonesian province Yogyakarta. They used three algorithms for machine-learning, namely Naïve Bayes (NB), a Support Vector Machine (SVM), and a Decision Tree (DT).…”

Section: Sentiment Analysis In Trafficmentioning

confidence: 99%

ATAM: Arabic Traffic Analysis Model for Twitter

AlFarasani¹,

AlHarthi²,

Alhumoud³

2019

ijacsa

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Harvesting Twitter for insight and meaning in what is called sentiment analysis (SA) is a major trend stemming from computational linguistics and AI. Industry and academia are interested in maximizing efficiency while mining text to attain the most currently available data and crowdsourcing opinions. In this study, we present the ATAM model for traffic analysis using the data available on Twitter. The model comprises five components that start with data streaming and collection and ends with the road incident prediction through classification. The classification of data is done using a lexiconbased method. The predicted classes are as follows: safe, needs attention, dangerous, and neutral. The data were collected for three months in the city of Riyadh, Saudi Arabia. The model was applied on 10k tweets with an overall accuracy of the model classifying all four classes of 82%.

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“…Tapan [7] purposed technique of EFWS (Effective Word Score) of tweets to improve sentiment analysis along with machine learning algorithm. Authors [9]- [11] [16] compares classification performances of machine learning algorithms namely SVM, Decision tree and naïve bayes and concluded that SVM approach works best. Yun [17] used ensemble sentiment classification based on majority vote principle of ensemble classification and results have shown that ensemble approach improves the accuracy in twitter sentiment classification.…”

Section: Literature Surveymentioning

confidence: 99%

“…In order to address the above problems and to get useful and meaningful information from the vast amounts of tweets, tweet data needs to be preprocessed To classify trending topics into particular category like: arts, books, health, music, politics, religion, science, sports common techniques used are Bag-of-Words technique, social network information, and network-based classification. Various machine learning algorithms [9] like Naive Bayes, SVM, random forest and Maximum Entropy are also used for categorization of tweets. This paper mainly focuses on classification of tweets into particular category by improving the performance metrics of various machine learning algorithms and a comparison between machine learning algorithms and liblinear classifier is also performed.…”

Section: Introductionmentioning

confidence: 99%

Categorisation of Tweets Using Ensemble Classification Methods

Mohanavalli¹,

Karthika²,

Srividya³

et al. 2018

IJET

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Twitter is a micro-blogging site that facilitates users to exchange short messages. Twitter is predominantly used in fields like business, healthcare, education and nation security. Twitter is being used by a large number of users for updating real time information and sentiment expression. The objective of this paper is to automate the classification of tweets into particular category using various machine learning algorithms like naïve bayes, SVM, and linear regression model. The proposed ensemble model aims to improve performance metrics of these algorithms. A comparative study of the algorithms used for tweet classification is done and results are discussed in the paper.

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Real-time traffic classification with Twitter data mining

Cited by 31 publications

References 8 publications

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

ATAM: Arabic Traffic Analysis Model for Twitter

Categorisation of Tweets Using Ensemble Classification Methods

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