Developing a Twitter-based traffic event detection model using deep learning architectures

Dabiri, Sina; Heaslip, Kevin

doi:10.1016/j.eswa.2018.10.017

Cited by 95 publications

(59 citation statements)

References 10 publications

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“…Automated feature learning methods such as deep learning architectures is a remedy to the above-mentioned shortcomings. Recently, researchers have shown an increased interest in leveraging deep learning algorithms for addressing challenging transportation-related problems [5,36]. first step is to partition the GPS trajectory of a trip into segments, in which every GPS segment contains only one transportation mode.…”

Section: Research Motivation and General Frameworkmentioning

confidence: 99%

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Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data

Dabiri

Heaslip

et al. 2020

IEEE Trans. Knowl. Data Eng.

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Identifying users' transportation modes (e.g., bike, bus, train, and car) is a key step towards many transportation related problems including (but not limited to) transport planning, transit demand analysis, auto ownership, and transportation emissions analysis. Traditionally, the information for analyzing travelers' behavior for choosing transport mode(s) was obtained through travel surveys. High cost, low-response rate, time-consuming manual data collection, and misreporting are the main demerits of the survey-based approaches. With the rapid growth of ubiquitous GPS-enabled devices (e.g., smartphones), a constant stream of users' trajectory data can be recorded. A user's GPS trajectory is a sequence of GPS points, recorded by means of a GPS-enabled device, in which a GPS point contains the information of the device geographic location at a particular moment. In this research, users' GPS trajectories, rather than traditional resources, are harnessed to predict their transportation mode by means of statistical models. With respect to the statistical models, a wide range of studies have developed travel mode detection models using on hand-designed attributes and classical learning techniques. Nonetheless, hand-crafted features cause some main shortcomings including vulnerability to traffic uncertainties and biased engineering justification in generating effective features. A potential solution to address these issues is by leveraging deep learning frameworks that are capable of capturing abstract features from the raw input in an automated fashion. Thus, in this thesis, deep learning architectures are exploited in order to identify transport modes based on only raw GPS tracks. It is worth noting that a significant portion of trajectories in GPS data might not be annotated by a transport mode and the acquisition of labeled data is a more expensive and labor-intensive task in comparison with collecting unlabeled data. Thus, utilizing the unlabeled GPS trajectory (i.e., the GPS trajectories that have not been annotated by a transport mode) is a cost-effective approach for improving the prediction quality of the travel mode detection model. Therefore, the unlabeled GPS data are also leveraged by developing a novel deep-learning architecture that is capable of extracting information from both labeled and unlabeled data. The experimental results demonstrate the superiority of the proposed models over the state-of-the-art methods in literature with respect to several performance metrics.

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Section: Research Motivation and General Frameworkmentioning

confidence: 99%

“…For every type of motion feature, a sequence can be created by placing the corresponding value for every GPS point of a SE in chronological order, where the feature value is computed using Eqs. (1)- (5). Such a sequence can be seen as a 1-d channel.…”

Section: New Representation For Raw Gps Segmentsmentioning

confidence: 99%

Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data

Dabiri

Heaslip

et al. 2020

IEEE Trans. Knowl. Data Eng.

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“…Many researchers pay more attention to the detection algorithm. Dabiri et al [23] utilize the deep learning architectures for detecting traffic incidents. Saeed et al [24] propose a novel approach named Weighted Dynamic Heartbeat Graph (WDHG) to detect events from the Twitter stream.…”

Section: Event Detection On Twittermentioning

confidence: 99%

Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM

et al. 2020

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In the context of increasing cyber threats and attacks, monitoring and analyzing network security incidents in a timely and effective way is the key to ensuring network infrastructure security. As one of the world’s most popular social media sites, users post all kinds of messages on Twitter, from daily life to global news and political strategy. It can aggregate a large number of network security-related events promptly and provide a source of information flow about cyber threats. In this paper, for detecting cyber threat events on Twitter, we present a multi-task learning approach based on the natural language processing technology and machine learning algorithm of the Iterated Dilated Convolutional Neural Network (IDCNN) and Bidirectional Long Short-Term Memory (BiLSTM) to establish a highly accurate network model. Furthermore, we collect a network threat-related Twitter database from the public datasets to verify our model’s performance. The results show that the proposed model works well to detect cyber threat events from tweets and significantly outperform several baselines.

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“…Although existing DNN models outperform traditional models, the spatial and temporal characteristics of traffic flow must be fully utilized to improve performance. In their study, Dabiri and Heaslip used twitter's spatial and temporal characteristics as data for predicting traffic flow density [60]. Mackenzie et al [61] developed a hybrid LSTM model for traffic density prediction in South Australia based on actual data from the Sydney Coordinated Adaptive Traffic System.…”

Section: Predictionmentioning

confidence: 99%

İnşaat Mühendisliğinde Derin Öğrenme Algoritmalarının Değerlendirilmesi ve Uygulanması

Çakiroğlu¹,

Süzen²

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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In this study, the applicability of deep learning algorithms in the field of civil engineering has been investigated. Firstly, the information that is about deep learning algorithms has been given. Additionally, deep learning applications, which are made, in subjects such as classification, estimation, and interpretation in the field of civil engineering, have been examined. The applications are elaborated according to civil engineering's sub-branches that transportation, geotechnical and construction. The contributions of the realized applications' in view of success rates to civil engineering that were analyzed. As a result of the study, it is foreseen that in the studies where the number of data is high, high performance will be achieved in the use of deep algorithms.

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Developing a Twitter-based traffic event detection model using deep learning architectures

Cited by 95 publications

References 10 publications

Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data

Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data

Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM

İnşaat Mühendisliğinde Derin Öğrenme Algoritmalarının Değerlendirilmesi ve Uygulanması

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