Travel time prediction with support vector regression

Wu, Chun-Hsin; Wei, Chia-Chen; Su, Da-Chun; Chang, Ming Chau; Ho, Jan-Ming

doi:10.1109/itsc.2003.1252721

Cited by 47 publications

(13 citation statements)

References 10 publications

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“…Linear regression analysis method [4,5], time series method [6][7][8], and space time prediction methods [9,10] are statistical models. Kalman filtering model [2,11,12], support vector regression model [13][14][15], and neural network model [16][17][18][19][20] are machine learning models. A series of combination models [21][22][23][24][25] are proposed in recent years.…”

Section: Introductionmentioning

confidence: 99%

Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches

Zou

et al. 2020

Journal of Advanced Transportation

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Freeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the impacts of different periodic functions on statistical and machine learning models. In this paper, our primary objective is to evaluate the performance of the six commonly used multistep ahead travel time prediction models (three statistical models and three machine learning models). In addition, we compared the impacts of three periodic functions on multistep ahead travel time prediction for different temporal scales (5-minute, 10-minute, and 15-minute). The results indicate that the periodic functions can improve the prediction performance of machine learning models for more than 60 minutes ahead prediction and improve the over 30 minutes ahead prediction accuracy for statistical models. Three periodic functions show a slight difference in improving the prediction accuracy of the six prediction models. For the same prediction step, the effect of the periodic function is more obvious at a higher level of aggregation.

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Section: Introductionmentioning

confidence: 99%

Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches

Zou

et al. 2020

Journal of Advanced Transportation

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“…SVM regression introduces an epsilon-insensitive loss function that is taken into account when minimizing the error through hyperplane optimization. SVM find their application for example in the field of financial forecasting (Trafalis & Ince, 2000), travel time prediction (Chun-Hsin Wu et al, 2003), flood forecasting (Yu, Chen & Chang, 2006) and genetics .…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Improving clinical refractive results of cataract surgery by machine learning (v0.1)"

2019

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To evaluate potential of Support Vector Machine Regression model (SVM-RM) and Multilayer Neural Network Ensemble model (MLNN-EM) as an intraocular lens (IOL) power calculation improvement for clinical workflow. Background. Current IOL power calculation methods offer limited accuracy and especially in eyes with an unusual ocular dimension the accuracy may decrease. In case of an improperly calculated power of the IOL in cataract or refractive lens replacement surgery, there is a risk of re-operation or further refractive correction. This may potentially induce complications and discomfort to the patient. Methods. A dataset containing information about 2194 eyes was obtained using data mining process from Electronic Health Record (EHR) system database of Gemini Eye Clinic. The dataset was optimized and split into Selection set (used in design of models and training), and Verification set (used in the evaluation). Set of mean prediction errors and distribution of predicted refractive error were evaluated for both models and Clinical results. Results. Both models performed significantly better for the most evaluated parameters compared to the clinical results. There was no significant difference between both evaluated models. In the ± 0.50 D prediction error category both SVM-RM and MLNN-EM were slightly better than Barrett Universal II formula (which is often presented as the most accurate calculation formula). Conclusion. In comparison to the method currently used in a clinical setting, both SVM-RM and MLNN-EM has achieved significantly better results in IOL calculations and therefore there is a strong potential to improve clinical cataract refractive outcomes.

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“…In the context of Geo-spatial data, many techniques have been explored previously such as Hidden Markov models [8], Kalman filter [3], support vector regression (SVR) [10], K-Nearest Neighbor (k-NN) [7] models, and data-driven models, such as artificial neural networks (ANNs) [9].…”

Section: Introductionmentioning

confidence: 99%

Real-time Destination and ETA Prediction for Maritime Traffic

Bodunov

Schmidt

Martin

et al. 2018

Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems

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In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for ( ) a destination and the ( ) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in mins) for the ETA prediction.ACM Reference Format:

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Travel time prediction with support vector regression

Cited by 47 publications

References 10 publications

Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches

Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches

Peer Review #1 of "Improving clinical refractive results of cataract surgery by machine learning (v0.1)"

Real-time Destination and ETA Prediction for Maritime Traffic

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