@article{Shen2026, 
author = {Yifei Shen and Wenlong Shi and Jiaxing Shen and Hengzhi Wang and Hanqing Wu and Jiannong Cao},
title = {Multiresolution Taxi Demand Prediction: A Big Data Statistical and Zero-Inflated Spatiotemporal GNN Approach},
year = {2026},
journal = {Big Data Mining and Analytics},
volume = {9},
number = {1},
pages = {39-56},
keywords = {urban transportation, statistical big data analytics, taxi demand prediction, multi-resolution prediction, data sparsity, Zero-Inflated Poisson (ZIP) distribution},
url = {https://www.sciopen.com/article/10.26599/BDMA.2025.9020069},
doi = {10.26599/BDMA.2025.9020069},
abstract = {Urban taxi demand prediction faces a critical resolution paradox: high-resolution forecasts enable operational agility but suffer from extreme sparsity-induced volatility, while low-resolution predictions sacrifice responsiveness for stability. We present a Scalable SpatioTemporal Zero-Inflated Poisson Graph Neural Network (SSTZIP-GNN), that resolves this paradox through three innovations: (1) Zero-Inflated Poisson (ZIP) integration that explicitly models structural zeros in sparse demand distributions, distinguishing genuine low-demand periods from data artifacts; (2) Adaptive spatiotemporal learning that dynamically adjusts kernel dilation factors and graph diffusion rates across temporal resolutions using Diffusion Graph Convolutional Networks (DGCNs) and Temporal Convolutional Networks (TCNs); (3) Multimodal feature fusion incorporating real-time crowd-sourced mobility data, socioeconomic indicators, and Global Position System (GPS) trajectories for enhanced robustness under variable urban conditions. Extensive evaluation on 130 million real-world mobility records demonstrates superior performance, achieving 34.8% Mean Absolute Error (MAE) reduction over state-of-the-art baselines. The model reduces computational costs by 46.3% compared to ensemble approaches while maintaining high accuracy across resolutions, delivering 33.4%−53.3% Root Mean Square Error (RMSE) reduction across different prediction resolution scenarios. This unified framework enables cities to implement demand-responsive fleet management, dynamic pricing, and sustainable mobility planning across diverse urban landscapes.}
}