XRMDN: A Novel Deep Learning Framework for Probabilistic Demand Forecasting in MoD Systems with High Volatility

Core Concepts

XRMDN is a sophisticated deep learning framework designed to accurately forecast probabilistic demand in high-volatility Mobility-on-Demand (MoD) systems, surpassing traditional forecasting models and enhancing operational efficiency.

Abstract

XRMDN introduces a novel approach to address the challenges of forecasting rider demand in MoD systems with high volatility. By leveraging a Gaussian mixture model and integrating recurrent neurons, XRMDN outperforms traditional statistical, machine learning, and deep learning models. The model's ability to capture demand trends and dependencies leads to more accurate predictions, particularly in scenarios of high demand volatility.

Stats

XRMDN significantly reduces the rejection rate under different percentiles compared to DeepAR. XRMDN outperforms ARIMA and ARIMA-GARCH in terms of log-likelihood values on various test sets. XRMDN shows improved performance in MAE, RMSE, and MAPE compared to LSTM and LightGBM on the bike-sharing dataset.

Quotes

"We propose an Extended Recurrent Mixture Density Network (XRMDN), a novel deep learning framework engineered to address these challenges." "Our comprehensive experimental analysis demonstrates that XRMDN surpasses existing benchmark models across various metrics."

Key Insights Distilled From

XRMDN

by Xiaoming Li,... at arxiv.org 03-06-2024

https://arxiv.org/pdf/2310.09847.pdf

Deeper Inquiries

How can the integration of GARCH methodologies within XRMDN enhance its forecasting accuracy

Integrating GARCH methodologies within XRMDN can significantly enhance its forecasting accuracy by addressing the limitations of traditional ARIMA models. GARCH models are specifically designed to capture the conditional variance in time series data, which is crucial for predicting high-volatility demand scenarios accurately. By incorporating GARCH into XRMDN, the model can better account for the dynamic and unpredictable fluctuations in demand levels that are common in Mobility-on-Demand (MoD) systems. This integration allows XRMDN to not only forecast mean values but also consider the variance of the demand distribution, leading to more robust and precise predictions.

What are the implications of XRMDN producing demand distributions as outputs for operational decision-making

The implications of XRMDN producing demand distributions as outputs are profound for operational decision-making in MoD systems. By providing probabilistic forecasts rather than point estimates, XRMDN offers a more comprehensive view of future demand scenarios. These demand distributions enable decision-makers to assess uncertainty levels associated with different outcomes, allowing them to make informed decisions based on risk tolerance and strategic objectives. Operational strategies such as fleet management, resource allocation, pricing optimization, and service planning can benefit from these probabilistic forecasts by considering a range of possible demand scenarios and their associated probabilities.

How does XRMDN's ability to handle high-demand volatility impact strategic planning in MoD systems

XRMDN's ability to handle high-demand volatility has significant implications for strategic planning in MoD systems. In environments characterized by fluctuating and unpredictable rider demands, accurate forecasting becomes essential for optimizing operations and enhancing customer satisfaction. By effectively capturing trends and dependencies in high-volatility demand sequences, XRMDN enables operators to anticipate sudden spikes or drops in rider requests more effectively. This capability allows MoD providers to adjust their services proactively, ensuring adequate resources are available during peak periods while avoiding overcapacity during low-demand times. Strategic planning initiatives such as capacity management, scheduling optimization, dynamic pricing strategies can benefit from XRMDN's enhanced forecasting precision under volatile conditions.