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Traffic congestion prediction based on GPS trajectory data

Author

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  • Shuming Sun
  • Juan Chen
  • Jian Sun

Abstract

Since speed sensors are not as widely used as GPS devices, the traffic congestion level is predicted based on processed GPS trajectory data in this article. Hidden Markov model is used to match GPS trajectory data to road network and the average speed of road sections can be estimated by adjacent GPS trajectory data. Four deep learning models including convolutional neural network, recurrent neural network, long short-term memory, and gated recurrent unit and three conventional machine learning models including autoregressive integrated moving average model, support vector regression, and ridge regression are used to perform congestion level prediction. According to the experimental results, deep learning models obtain higher accuracy in traffic congestion prediction compared with conventional machine learning models.

Suggested Citation

  • Shuming Sun & Juan Chen & Jian Sun, 2019. "Traffic congestion prediction based on GPS trajectory data," International Journal of Distributed Sensor Networks, , vol. 15(5), pages 15501477198, May.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:5:p:1550147719847440
    DOI: 10.1177/1550147719847440
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    References listed on IDEAS

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    1. (Sean) Qian, Zhen & Li, Jia & Li, Xiaopeng & Zhang, Michael & Wang, Haizhong, 2017. "Modeling heterogeneous traffic flow: A pragmatic approach," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 183-204.
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    Cited by:

    1. Balaji Ganesh Rajagopal & Manish Kumar & Pijush Samui & Mosbeh R. Kaloop & Usama Elrawy Shahdah, 2022. "A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    2. Chen, Liao & Ma, Shoufeng & Li, Changlin & Yang, Yuance & Wei, Wei & Cui, Runbang, 2024. "A spatial–temporal graph-based AI model for truck loan default prediction using large-scale GPS trajectory data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    3. Gary Reyes & Roberto Tolozano-Benites & Laura Lanzarini & César Estrebou & Aurelio F. Bariviera & Julio Barzola-Monteses, 2023. "Methodology for the Identification of Vehicle Congestion Based on Dynamic Clustering," Sustainability, MDPI, vol. 15(24), pages 1-18, December.
    4. Felipe Lagos & Sebastián Moreno & Wilfredo F. Yushimito & Tomás Brstilo, 2024. "Urban Origin–Destination Travel Time Estimation Using K-Nearest-Neighbor-Based Methods," Mathematics, MDPI, vol. 12(8), pages 1-18, April.

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