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Urban Traffic Congestion Prediction Using Floating Car Trajectory Data

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9529)


Traffic congestion prediction is an important precondition to promote urban sustainable development. Nevertheless, there is a lack of a unified prediction method to address the performance metrics, such as accuracy, instantaneity and stability, systematically. In the paper, we propose a novel approach to predict the urban traffic congestion efficiently with floating car trajectory data. Specially, an innovative traffic flow prediction method utilizing particle swarm optimization algorithm is responsible for calculating the traffic flow parameters. Then, a congestion state fuzzy division module is applied to convert the predicted flow parameters to citizens’ cognitive congestion states. We conduct extensive experiments with real floating car data and the experimental results show that our proposed method has advantage in terms of accuracy, instantaneity and stability.


  • Floating car
  • Particle swarm optimization
  • Traffic congestion prediction
  • Traffic flow prediction
  • Fuzzy comprehensive evaluation

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This work was partially supported by the Natural Science Foundation of China under Grants No. 61203165 and No. 61174174, the Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, P.R. China No. SCIP2012001, and the Fundamental Research Funds for Central Universities.

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Correspondence to Xiangjie Kong .

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Yang, Q., Wang, J., Song, X., Kong, X., Xu, Z., Zhang, B. (2015). Urban Traffic Congestion Prediction Using Floating Car Trajectory Data. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9529. Springer, Cham.

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