Toward a real‐time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning

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

SelfMatch: Robust semisupervised time‐series classification with self‐distillation

“…Time‐series data have been useful in many application fields, such as anomaly detection, 1 traffic prediction, 2 power cycle prediction, 3 service preference matching, 4 and abstractive summarization 5 . A time‐series classification (TSC) algorithm needs to mine both local and global patterns of data to diversify various types of features.…”