STZIP-GNN: A Robust Model for Taxi Demand Prediction in Sparse Urban Environments

Accurate prediction of taxi demand is crucial for optimizing urban transportation systems, improving passenger experiences, and ensuring efficient resource allocation. However, few studies pay close attention to the issue of data sparsity, particularly in high temporal-spatial resolution data, which presents a significant challenge due to the large number of zeros that can affect model prediction performance. Traditional methods predominantly rely on historical order data for predictions, often failing to capture dynamic and contextual information effectively. To address this problem, we propose a spatiotemporal Zero-Inflated Poisson Graph Neural Network (STZIP-GNN) to enhance prediction performance. Our approach leverages the Zero-Inflated Poisson (ZIP) distribution to effectively capture the large number of zeros in sparse data and incorporates additional richer data sources, such as crowdsensing geolocation data, to mitigate the impact of data sparsity on the model. By utilizing the representational power of spatiotemporal graph neural networks, our model fits the parameters of the probability distribution, enhancing prediction performance. Experimental results demonstrate that our model outperforms other baseline models and validates its effectiveness on real-world datasets.