Real-Time Short-Term Traffic Flow Forecasting Based on Process Neural Network
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Kernel Regression with a Mahalanobis Metric for Short-Term Traffic Flow Forecasting
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Statistical methods to estimate vehicle count using traffic cameras
Multidimensional Systems and Signal Processing
Maintaining consistency of vague databases using data dependencies
Data & Knowledge Engineering
Traffic flow forecasting based on multitask ensemble learning
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Traffic Density Estimation with On-line SVM Classifier
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
An aggregation approach to short-term traffic flow prediction
IEEE Transactions on Intelligent Transportation Systems
PPCA-based missing data imputation for traffic flow volume: a systematical approach
IEEE Transactions on Intelligent Transportation Systems
On Optimization of Predictions in Ontology-Driven Situation Awareness
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Collective traffic forecasting
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Sparse kernel regression for traffic flow forecasting
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Application of interval type-2 fuzzy neural networks to predict short-term traffic flow
International Journal of Computer Applications in Technology
GCBN: a hybrid spatio-temporal causal model for traffic analysis and prediction
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Application of Bayesian Networks for Autonomic Network Management
Journal of Network and Systems Management
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A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist. Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data