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Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

Author

Listed:
  • Zhaosheng Yang
  • Duo Mei
  • Qingfang Yang
  • Huxing Zhou
  • Xiaowen Li

Abstract

To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM) model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface). The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

Suggested Citation

  • Zhaosheng Yang & Duo Mei & Qingfang Yang & Huxing Zhou & Xiaowen Li, 2014. "Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, August.
    • Handle: RePEc:hin:jnlmpe:926251
    DOI: 10.1155/2014/926251
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    Cited by:

    1. He, Yuxin & Zhao, Yang & Luo, Qin & Tsui, Kwok-Leung, 2022. "Forecasting nationwide passenger flows at city-level via a spatiotemporal deep learning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).

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