Travel-Time Prediction With Support Vector Regression

Wu, Chun-Hsin; Ho, Jan-Ming; Lee, D.T.

doi:10.1109/tits.2004.837813

Cited by 1,196 publications

(563 citation statements)

References 15 publications

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“…The past decades have witnessed an increasing passion for the research of bus running time prediction. The literature focuses on time series [1] artificial neural network or support vector machine (SVM) [2][3][4][5][6][7][8] and Kalman filtering techniques [9,10], etc.…”

Section: S Zhong Et Al: a Hybrid Model Based On Support Vector Machmentioning

confidence: 99%

“…SVM was developed by Vapnik [14,15], which is characterized by a specific type of learning algorithms. It has been successfully applied to solve some classic problems, such as incident detection [16], traffic-pattern recognition [17], passenger head recognition [18], and travel time prediction [7,8,[19][20][21]. SVM is used to find regular pattern and makes use of them to analyse the unknowns.…”

Section: S Zhong Et Al: a Hybrid Model Based On Support Vector Machmentioning

confidence: 99%

“…In addition, there remains the advantage of simpliness. Moreover, the adaptive algorithm overcomes the problem of lacking of an appropriate support vector in a standard SVM [8,29]. Yao et al [30] applied the adaptive algorithm to adjust the performance of SVM method.…”

Section: S Zhong Et Al: a Hybrid Model Based On Support Vector Machmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

Zhong¹,

et al. 2015

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Section: S Zhong Et Al: a Hybrid Model Based On Support Vector Machmentioning

confidence: 99%

Section: S Zhong Et Al: a Hybrid Model Based On Support Vector Machmentioning

confidence: 99%

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A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

Zhong¹,

et al. 2015

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“…On the other hand there are several methods found in literature based on ML and statistics which have the ability to provide innovative and predictive solutions in different domains, for example predicting passengers travel time for ITS [8] and energy demand for buildings [9] are two of the many examples found in literature. However, they are not suitable for analyzing and correlating different data streams in near realtime as they require historical data to train the models.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analytics for Complex IoT Data Streams

Akbar

Khan

Carrez

et al. 2017

IEEE Internet Things J.

116

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Abstract-The requirements of analyzing heterogeneous data streams and detecting complex patterns in near real-time have raised the prospect of Complex Event Processing (CEP) for many internet of things (IoT) applications. Although CEP provides a scalable and distributed solution for analyzing complex data streams on the fly, it is designed for reactive applications as CEP acts on near real-time data and does not exploit historical data. In this regard, we propose a proactive architecture which exploits historical data using machine learning (ML) for prediction in conjunction with CEP. We propose an adaptive prediction algorithm called Adaptive Moving Window Regression (AMWR) for dynamic IoT data and evaluated it using a real-world use case with an accuracy of over 96%. It can perform accurate predictions in near real-time due to reduced complexity and can work along CEP in our architecture. We implemented our proposed architecture using open source components which are optimized for big data applications and validated it on a use-case from Intelligent Transportation Systems (ITS). Our proposed architecture is reliable and can be used across different fields in order to predict complex events.

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“…In addition, there have been many attempts to estimate future conditions using data mining. Some are parametric linear and non-linear regression models [7][8][9][10], nonparametric regression models [11], ARIMA models [12], space-time ARIMA models [13][14][15], ATHENA models [16], Kalman filters [17], artificial neural networks [18][19][20][21][22], and support vector machines [23]. Emerging traffic data collection techniques make these extrapolation-based models easier to use.…”

Section: Introductionmentioning

confidence: 99%