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Analysis of road traffic speed in Kunming plateau mountains: a fusion PSO-LSTM algorithm

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  • Yao Mao
  • Guojin Qin
  • Pingan Ni
  • Qian Liu

Abstract

Traffic operation quality of road network plays a vital role in urban planning and sustainable development. In this work, taking Kunming City, China as an example, the road traffic speed analysis method for plateau mountain area was developed combined with Python tools. Thereby, a one-stop analysis framework was proposed based on road traffic data of the downtown area of Kunming City. Specifically, a PSO-LSTM based model was developed to predict the whole and the dynamic time series of road traffic speed.. Then, the appearance of rush hour is discussed from the local and static heat map. Finally, the primary road traffic conditions of Kunming city were investigated, combined with the characteristics of the plateau mountain areas. The results show that the overall traffic situation in Kunming city is slow, and the partial traffic situation is congested in the east and west region. The crowding status is closely related to the peak working hours of Kunming residents, the geographical characteristics of plateau mountain areas, and the planning and distribution of residential land and working land.Highlights A Python-based one-stop framework is developed to analyse road speeds and visualize the results.A PSO-LSTM algorithm model is developed for road traffic speed prediction.This work provides a reference for traffic planning and road design in plateau mountain cities.

Suggested Citation

  • Yao Mao & Guojin Qin & Pingan Ni & Qian Liu, 2022. "Analysis of road traffic speed in Kunming plateau mountains: a fusion PSO-LSTM algorithm," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 26(1), pages 87-107, January.
    • Handle: RePEc:taf:rjusxx:v:26:y:2022:i:1:p:87-107
    DOI: 10.1080/12265934.2021.1882331
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    Cited by:

    1. Naheliya, Bharti & Redhu, Poonam & Kumar, Kranti, 2024. "MFOA-Bi-LSTM: An optimized bidirectional long short-term memory model for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    2. Shasha Yang & Anjie Jin & Wen Nie & Cong Liu & Yu Li, 2022. "Research on SSA-LSTM-Based Slope Monitoring and Early Warning Model," Sustainability, MDPI, vol. 14(16), pages 1-16, August.

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