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In the realm of Mobility-on-Demand (MoD) systems, the forecasting of riderdemand is a cornerstone for operational decision-making and systemoptimization. Traditional forecasting methodologies primarily yield pointestimates, thereby neglecting the inherent uncertainty within demandprojections. Moreover, MoD demand levels are profoundly influenced by bothendogenous and exogenous factors, leading to high and dynamic volatility. Thisvolatility significantly undermines the efficacy of conventional time seriesforecasting methods. In response, we propose an Extended Recurrent MixtureDensity Network (XRMDN), a novel deep learning framework engineered to addressthese challenges. XRMDN leverages a sophisticated architecture to processdemand residuals and variance through correlated modules, allowing for theflexible incorporation of endogenous and exogenous data. This architecture,featuring recurrent connections within the weight, mean, and variance neuralnetworks, adeptly captures demand trends, thus significantly enhancingforecasting precision, particularly in high-volatility scenarios. Ourcomprehensive experimental analysis, utilizing real-world MoD datasets,demonstrates that XRMDN surpasses the existing benchmark models across variousmetrics, notably excelling in high-demand volatility contexts. This advancementin probabilistic demand forecasting marks a significant contribution to thefield, offering a robust tool for enhancing operational efficiency and customersatisfaction in MoD systems.

XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatility