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A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location

Author

Listed:
  • Peixin Dong

    (School of Microelectronics, Shandong University, Jinan 250100, China)

  • Dongyuan Li

    (School of Microelectronics, Shandong University, Jinan 250100, China)

  • Jianping Xing

    (School of Microelectronics, Shandong University, Jinan 250100, China)

  • Haohui Duan

    (Traffic Police Detachment, Jinan Public Security Bureau, Jinan 250100, China)

  • Yong Wu

    (Jinan Public Transportation Corporation, Jinan 250100, China)

Abstract

Aiming at the problems of poor time performance and accuracy in bus stops network optimization, this paper proposes an algorithm based on complex network and graph theory and Beidou Vehicle Location to measure the importance of bus stops. This method narrows the scope of points and edges to be optimized and is applied to the Jinan bus stop network. In this method, the bus driving efficiency, which can objectively reflect actual road conditions, is taken as the weight of the connecting edges in the network, and the network is optimized through the network efficiency. The experimental results show that, compared with the original network, the optimized network time performance is good and the optimized network bus driving efficiency is improved.

Suggested Citation

  • Peixin Dong & Dongyuan Li & Jianping Xing & Haohui Duan & Yong Wu, 2019. "A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location," Future Internet, MDPI, vol. 11(4), pages 1-12, April.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:4:p:97-:d:222812
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    References listed on IDEAS

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    Cited by:

    1. Xin Zhou & Peixin Dong & Jianping Xing & Peijia Sun, 2019. "Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network," Future Internet, MDPI, vol. 11(12), pages 1-11, November.

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