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An adaptive hybrid model for short-term urban traffic flow prediction

Author

Listed:
  • Hou, Qinzhong
  • Leng, Junqiang
  • Ma, Guosheng
  • Liu, Weiyi
  • Cheng, Yuxing

Abstract

With the rapid increase in car ownership, urban transport systems are challenged by the overwhelming traffic demand and congestion. Dynamic prediction of traffic flows is of considerable significance for congestion mitigation and demand management. Real-time and precise prediction models are capable of analyzing traffic flow characteristics, predicting traffic flow trends, and motivating reasonable inductive actions. Considering the periodicity and variability of traffic flow and limitations of single prediction models, an adaptive hybrid model for predicting short-term traffic flow was proposed in this study. Firstly, the linear Autoregressive Integrated Moving Average (ARIMA) method and non-linear Wavelet Neural Network (WNN) method were used to predict traffic flow. Then, outputs of the two individual models were analyzed and combined by fuzzy logic and the weighted result was regarded as the final predicted traffic volume of the hybrid model. The results indicate that the hybrid model can offer better performance in predicting short-term traffic flow than the two single models either in stable or in fluctuating conditions. The relative error is within ±10%, showing that the proposed hybrid model is both accurate and reliable.

Suggested Citation

  • Hou, Qinzhong & Leng, Junqiang & Ma, Guosheng & Liu, Weiyi & Cheng, Yuxing, 2019. "An adaptive hybrid model for short-term urban traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119306508
    DOI: 10.1016/j.physa.2019.121065
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    Citations

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    Cited by:

    1. Wei Zhou & Wei Wang & Xuedong Hua & Yi Zhang, 2020. "Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition," Sustainability, MDPI, vol. 12(15), pages 1-23, July.
    2. Sun, Li & Zhao, Juanjuan & Zhang, Jun & Zhang, Fan & Ye, Kejiang & Xu, Chengzhong, 2024. "Activity-based individual travel regularity exploring with entropy-space K-means clustering using smart card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    3. Ismail Shah & Izhar Muhammad & Sajid Ali & Saira Ahmed & Mohammed M. A. Almazah & A. Y. Al-Rezami, 2022. "Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    4. 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).
    5. Peng, Yanni & Xiang, Wanli, 2020. "Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    6. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K. & Cao, Shuhan, 2023. "A generative model for vehicular travel time distribution prediction considering spatial and temporal correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    7. Lu, Wenqi & Yi, Ziwei & Wu, Renfei & Rui, Yikang & Ran, Bin, 2022. "Traffic speed forecasting for urban roads: A deep ensemble neural network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    8. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K., 2024. "A multi-task spatio-temporal generative adversarial network for prediction of travel time reliability in peak hour periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    9. Huang, Hai-chao & He, Hong-di & Zhang, Zhe & Ma, Qing-hai & Xue, Xing-kuo & Zhang, Wen-xiu, 2024. "Variable-length traffic state prediction and applications for urban network with adaptive signal timing plan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).

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