LSTM-autoencoder deep clustering of big GPS trajectory data for parking behavior classification

Certain types of driving behaviors (e.g., searching or cruising for a parking space) may contribute to a series of urban issues, including excessive searching for parking and traffic congestion, among others. While some studies have analyzed the impact of various factors on cruising times, fewer studies have developed methods for detecting different parking-related behaviors from trajectory datasets. In this research, using detailed driving behaviors captured by passively collected in-vehicle GPS trajectory data, we introduce a computing framework that extracts parking trajectories from driving trajectories and classifies them into cruising or direct parking categories. The framework consists of a preprocessing module, a rule-based distributed parking trajectory extraction module, and an LSTM-autoencoder deep clustering model. The preprocessing and parking trajectory extraction modules are designed to extract trajectories that involve parking from raw driving trajectories. The deep clustering model is designed to take feature-augmented parking trajectories of varying lengths, without labels, as input and return two broad categories representing cruising and direct parking. The model returned two types of cruising and two types of direct parking. The results reveal spatial differences in parking behaviors, with more cruising concentrated in commercial and entertainment areas of Washington, DC. This study contributes a scalable framework for extracting and classifying unlabeled parking trajectories from big GPS trajectory data. The framework can be applied to trajectory data collected for any city and will be useful for identifying locations in an urban environment with cruising times that could be reduced to ease traffic flow and parking demand.