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.
WSDOT Truck Parking Online Information Platform
Figure 1. The architecture of the proposed TPIMS in the pilot project. Six different parts are included in the system as follows: (a) illustration of the pilot TPIMS architecture; (b) radar-based wireless in-ground sensor made by the Sensys network; (c) the ﬁnished installation illustration of the in-ground radar sensor; (d) the ﬁnished installation of the wireless repeaters’ on the light pole; (e) the real-time surveillance video stream; (f) the real-time slot level parking status visualization website.
Yang, H., Liu, C., Zhuang, Y., Sun, W., Murthy, K., Pu, Z., & Wang, Y. (2021). Truck parking pattern aggregation and availability prediction by deep learning. IEEE Transactions on Intelligent Transportation Systems.
Murthy, K. & Yang, H. (2021) A Cost-Effective Solution For Truck Parking Based On Artificial Intelligence [PowerPoint Slides]. Western States Forum 2021, http://www.westernstatesforum.org/Documents/2021/Presentations/WSDOT_UW_MurthyYang_Final_TruckParkingPrediction.pdf.