MOAS'07 Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulation
Local prediction of non-linear time series using support vector regression
Pattern Recognition
Local prediction of non-linear time series using support vector regression
Pattern Recognition
Real-time prediction of order flowtimes using support vector regression
Computers and Operations Research
AADT prediction using support vector regression with data-dependent parameters
Expert Systems with Applications: An International Journal
Travel Speed Prediction Using Fuzzy Reasoning
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
A knowledge based real-time travel time prediction system for urban network
Expert Systems with Applications: An International Journal
Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions
Expert Systems with Applications: An International Journal
A dynamic holding strategy in public transit systems with real-time information
Applied Intelligence
MS '07 The 18th IASTED International Conference on Modelling and Simulation
IEEE Transactions on Intelligent Transportation Systems
Time series prediction using support vector machines: a survey
IEEE Computational Intelligence Magazine
Real-time foreground-background segmentation using adaptive support vector machine algorithm
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Multiscale wavelet support vector regression for traffic flow prediction
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Real-time traffic flow forecasting based on MW-AOSVR
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Expert Systems with Applications: An International Journal
IEEE Transactions on Intelligent Transportation Systems
Collective traffic forecasting
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Travel time prediction for dynamic routing using ant based control
Winter Simulation Conference
Improved travel time prediction algorithms for intelligent transportation systems
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Computer Methods and Programs in Biomedicine
Prediction of railway passenger traffic volume by means of LS-SVM
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
A heuristic weight-setting algorithm for robust weighted least squares support vector regression
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
A fast data preprocessing procedure for support vector regression
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
An adaptive resource management scheme in cloud computing
Engineering Applications of Artificial Intelligence
HTTP: a new framework for bus travel time prediction based on historical trajectories
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Forecasting building occupancy using sensor network data
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Probability tree based passenger flow prediction and its application to the Beijing subway system
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Travel time is a fundamental measure in transportation. Accurate travel-time prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. We apply support vector regression (SVR) for travel-time prediction and compare its results to other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that SVR will perform well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can significantly reduce both relative mean errors and root-mean-squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and performs well for traffic data analysis.