Toward a real‐time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning. Issue 8 (16th November 2021)

• Record Type: Journal Article
• Authors: Yang, Hao (Frank); Ke, Ruimin; Cui, Zhiyong; Wang, Yinhai; Murthy, Karthik
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 8(2022)
• Page Start: 4437
• Page End: 4470
• Publication Date: 2021-11-16
• Subjects: deep learning -- heterogeneous data -- latent feature embedding -- parking prediction -- representation learning -- smart parking system; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22725
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Managing and estimating the availability information of parking lots is of great importance to travelers and managers. However, the task is very challenging since the occupancy rate is affected by various factors, including spatial‐temporal features, parking lot attributes features, and environmental changes. Previous studies mostly focus on the short‐term prediction by capturing the historical sequential dependencies among inputs and outputs, which leads to low estimation accuracy for long‐term prediction and limited scalability for real deployment in parking lots. To address the challenges, a comprehensive framework for real‐time Smart Parking Data Management and Prediction (SPDMP) system is proposed. Three types of data sources, including historical sequential data, real‐time sequential data, and attributes category data, are sufficiently integrated into a customized Parking Availability Prediction (PAP) neural network by representation learning and heterogeneous feature embedding. Specifically, instead of using parking lot property information and environmental data directly, the authors design an Attributes Tensor Embedding Component (ATEC) to integrate the intra‐class affection and interclass representations and correlations by two steps of customized embedding process. To balance the impact of the indiscriminate features for various prediction targets, this study proposes a multi‐factor attention mechanism to learn the weights and help the PAP achieve a stable performance for time‐various tasks. From extensive experiments on two real‐world large‐scale data sets collected in China and USA (including 19 urban parking and 2 truck parking lots), PAP achieves superior prediction results on both urban parking (6.69% average MAPE) and truck parking prediction (7.63% average MAPE) for five prediction time slots (5 min, 10 min, 30 min, 1 h, and 2 h). Furthermore, even with limited training data, PAP still shows better transferability and adaptability for various types of lots. The proposed SPDMP is selected and used as a pilot test bed by the Washington State Department of Transportation (WSDOT) for truck parking information promotion.