Integrating Twitter Traffic Information with Kalman Filter Models for Public Transportation Vehicle Arrival Time Prediction

Abidin, Ahmad Faisal Amri; Kolberg, Mario; Hussain, Amir

doi:10.1007/978-3-319-25313-8_5

Cited by 15 publications

(8 citation statements)

References 5 publications

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“…Despite these drawbacks, many studies have attempted to incorporate tweet information in traffic prediction model training. For instance, [1] presented a Kalman Filter model trained using integrated twitter traffic information and traffic data in order to predict public vehicle arrival time. The study utilised real-time tweets related to road traffic information and performs semantic analysis on the retrieved dataset.…”

Section: Traffic Prediction Using Twitter Informationmentioning

confidence: 99%

“…The study incorporated tweet data to predict incoming traffic flow prior to sport game events, an approach that was evaluated using four models, namely ARIMA, neural network, support vector regression, and k-nearest neighbour (k-NN). A Kalman-filter model incorporating twitter data for real-time bus arrival prediction time is presented in [1].…”

Section: Introductionmentioning

confidence: 99%

“…Social media as an online discussion platform has seen a remarkable explosion in the last few years. Examples include Facebook, 1 Twitter, 2 Instagram, 3 and Snapchat. 4 These services are widely employed for communication, news reporting, and advertising events.…”

Section: Introductionmentioning

confidence: 99%

“…Twitter is a public social media platform popular for short messages (up to 280 characters), thereby resulting in data streams with high velocity and timely dissemination of information concerning real-world events. Given the enormity and variance of information obtainable on twitter due to the large user base, numerous studies have sought to harness this online data repository for various data mining purposes, such as stock market prices [29], crime rate prediction [39], and traffic prediction [1,15,41].…”

Section: Introductionmentioning

confidence: 99%

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A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

Petrounias²,

et al. 2020

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Short-term traffic parameter forecasting is critical to modern urban traffic management and control systems. Predictive accuracy in data-driven traffic models is reduced when exposed to non-recurring or non-routine traffic events, such as accidents, road closures, and extreme weather conditions. The analytical mining of data from social networksspecifically twittercan improve urban traffic parameter prediction by complementing traffic data with data representing events capable of disrupting regular traffic patterns reported in social media posts. This paper proposes a deep learning urban traffic prediction model that combines information extracted from tweet messages with traffic and weather information. The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom. The findings from extensive empirical analysis using real-world data demonstrate the effectiveness of the approach in improving prediction accuracy when compared to other classical/statistical and machine learning (ML) state-of-the-art models. The improvement in predictive accuracy can lead to reduced frustration for road users, cost savings for businesses, and less harm to the environment.

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Section: Traffic Prediction Using Twitter Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

Petrounias²,

et al. 2020

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“…On LBSN platforms, users check-in to places and publish details about their daily life, including experiences, opinions, and emotions in the form of attached text snippets and ratings. Recently, LBSNs and other forms of social media have attracted considerable research interest in the social and geospatial sciences, and were utilized in different fields such as human mobility studies (Cho, Myers, and Leskovec 2011;, location recommendation (Noulas et al 2012a;Ye, Yin, and Lee 2010), traffic forecasting (Abbasi et al 2015;Abidin, Kolberg, and Hussain 2015), event detection (Chen and Roy 2009;Gupta, Li, and Chang 2014), disaster management (de Albuquerque et al 2015;Kongthon et al 2012), and the public health domain (Burton et al 2012;Lee, Agrawal, and Choudhary 2013).…”

Section: Introductionmentioning

confidence: 99%

Do people communicate about their whereabouts? Investigating the relation between user-generated text messages and Foursquare check-in places

Westerholt

Zipf

2018

Geo-spatial Information Science

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The social functionality of places (e.g. school, restaurant) partly determines human behaviors and reflects a region's functional configuration. Semantic descriptions of places are thus valuable to a range of studies of humans and geographic spaces. Assuming their potential impacts on human verbalization behaviors, one possibility is to link the functions of places to verbal representations such as users' postings in location-based social networks (LBSNs). In this study, we examine whether the heterogeneous user-generated text snippets found in LBSNs reliably reflect the semantic concepts attached with check-in places. We investigate Foursquare because its available categorization hierarchy provides rich a-priori semantic knowledge about its check-in places, which enables a reliable verification of the semantic concepts identified from user-generated text snippets. A latent semantic analysis is conducted on a large Foursquare check-in dataset. The results confirm that attached text messages can represent semantic concepts by demonstrating their large correspondence to the official Foursquare venue categorization. To further elaborate the representativeness of text messages, this work also performs an investigation on the textual terms to quantify their abilities of representing semantic concepts (i.e., representativeness), and another investigation on semantic concepts to quantify how well they can be represented by text messages (i.e., representability). The results shed light on featured terms with strong locational characteristics, as well as on distinctive semantic concepts with potentially strong impacts on human verbalizations. ARTICLE HISTORY

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Hybrid Statistical and Machine Learning Methods for Road Traffic Prediction: A Review and Tutorial

Alsolami

Mehmood

Albeshri

2019

Smart Infrastructure and Applications

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Integrating Twitter Traffic Information with Kalman Filter Models for Public Transportation Vehicle Arrival Time Prediction

Cited by 15 publications

References 5 publications

A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

Do people communicate about their whereabouts? Investigating the relation between user-generated text messages and Foursquare check-in places

Hybrid Statistical and Machine Learning Methods for Road Traffic Prediction: A Review and Tutorial

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