Truck parking pattern aggregation and availability prediction by deep learning

Hao Yang, Chenxi Liu, Yifan Zhuang, Wei Sun, Karthik Murthy, Ziyuan Pu, Yinhai Wang

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)


With the significant increase of e-commerce, freight transportation demand has surged significantly over the past decade. Most of the demand has been served by trucks in the United States. One major problem commonly identified across the country is the worsening truck parking availability because the increase of truck parking facilities has lagged behind the growth of trucking activities. The lack of parking spaces and real-time parking availability information greatly exacerbate the uncertainty of trips, and often results in illegal and potentially dangerous parking or overtime driving. This paper elaborates on pilot research on improving truck parking facilities cooperated with the Washington State Department of Transportation (WSDOT), building and testing the advanced Truck Parking Information and Management System (TPIMS) with the real-time user visualization and prediction function empowered by artificial intelligence. Furthermore, by analyzing the activities of truck drivers, the researchers aggregated the regularity of truck parking patterns by a customized sequential similarity methodology. A Truck Parking Occupancy Prediction (TPOP) neural network for time-variant occupancy prediction by deep learning and attributes embedding is proposed and integrated into the TPIMS. The TPOP achieves 5.82%, 5.07%, 4.84%, and 4.19% mean average percentage error (MAPE) for 16, 8, 4, and 2 minutes ahead of occupancy prediction respectively, significantly outperforms other state-of-the-art methods. Clearly, the proposed solutions can benefit both the truck drivers and government agencies by a more efficient and smart TPIMS.

Original languageEnglish
Pages (from-to)12778-12789
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number8
Publication statusPublished - Aug 2022


  • Analytical models
  • deep learning
  • Meteorology
  • Neural networks
  • parking information system
  • parking prediction
  • pattern aggregation.
  • Predictive models
  • Real-time systems
  • Sensors
  • Truck parking
  • Vehicles

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