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A bayesian network approach to traffic flow forecasting

A bayesian network approach to traffic flow forecasting,10.1109/TITS.2006.869623,IEEE Transactions on Intelligent Transportation Systems,Shiliang Sun,

A bayesian network approach to traffic flow forecasting   (Citations: 49)
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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
Journal: IEEE Transactions on Intelligent Transportation Systems - TITS , vol. 7, no. 1, pp. 124-132, 2006
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    • ...We observe that, despite the prevalent use of the graph data structure [9], [14], [16], [24], [25] to model the road network topology, the RL and BP graph algorithms have seen little or no attention in traffic modeling...

    Andrew Phanet al. Interpolating Sparse GPS Measurements Via Relaxation Labeling and Beli...

    • ...Based on these collected data, many researchers work on traffic prediction [2] [3] and bus arrival time prediction [4] [5] [6]...

    Jiashun Liuet al. Bus Trip Planning Service Based on Real Time Data

    • ...Second, the infinite-mixture model and the corresponding variational approximation are, for the first time, applied to the traffic prediction problem and successfully outperform the state-ofthe-art Bayesian network (BN) approach [10]...
    • ...For the standard EM algorithm [8], [10], the expectation would be calculated over a true posterior distribution...
    • ...Traffic flow prediction, which is defined to be predicting future traffic flows of a certain road segment, is an important direction in the research of intelligent transportation systems [10], [25]‐[29]...
    • ...As done in [10] and [25], we carry out a onestep prediction, and the prediction time horizon is 15 min...
    • ...The BN approach [10] is one of the state-of-the-art methods for traffic flow prediction...

    Shiliang Sunet al. Variational Inference for Infinite Mixtures of Gaussian Processes With...

    • ...In this paper we focus on short-term traffic forecasting (typically on a time scale of 5-30 min [3]) as a fundamental tool for real-time traffic control...
    • ...Several approaches have been proposed in the literature based on non-parametric models, auto regressive integrated moving average models, Kalman filtering, neural networks, fuzzy logic or Bayesian models [2] [3]...
    • ...Recently some attempts to forecast traffic parameters using BN have been conducted [3] [5]...
    • ...In this section we employ a BN to perform traffic flow prediction following the approach proposed in [3]...
    • ...The latter is here modelled using GMM as in [3]:...

    A. Pascaleet al. Adaptive Bayesian network for traffic flow prediction

    • ...Evolutionary neural networks, fuzzy neural networks, and neural network ensemble algorithms have been tested for short term traffic flow forecasting problems [5-7]...

    Yisheng Anet al. Short-term traffic flow forecasting via echo state neural networks

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