Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks

TL;DR

This paper presents a Neurosymbolic AI framework that combines decision tree rules with neural networks to improve travel demand prediction, achieving better accuracy and interpretability than traditional methods.

Contribution

It introduces a novel integration of symbolic decision tree rules into neural networks for travel demand forecasting, enhancing both interpretability and predictive performance.

Findings

Enriched datasets with symbolic rules outperform standalone models.

Finer variance threshold rules improve prediction accuracy.

The approach balances interpretability with high predictive accuracy.

Abstract

Travel demand prediction is crucial for optimizing transportation planning, resource allocation, and infrastructure development, ensuring efficient mobility and economic sustainability. This study introduces a Neurosymbolic Artificial Intelligence (Neurosymbolic AI) framework that integrates decision tree (DT)-based symbolic rules with neural networks (NNs) to predict travel demand, leveraging the interpretability of symbolic reasoning and the predictive power of neural learning. The framework utilizes data from diverse sources, including geospatial, economic, and mobility datasets, to build a comprehensive feature set. DTs are employed to extract interpretable if-then rules that capture key patterns, which are then incorporated as additional features into a NN to enhance its predictive capabilities. Experimental results show that the combined dataset, enriched with symbolic rules, consistently outperforms standalone datasets across multiple evaluation metrics, including Mean Absolute Error (MAE), \(R^2\), and Common Part of Commuters (CPC). Rules selected at finer variance thresholds (e.g., 0.0001) demonstrate superior effectiveness in capturing nuanced relationships, reducing prediction errors, and aligning with observed commuter patterns. By merging symbolic and neural learning paradigms, this Neurosymbolic approach achieves both interpretability and accuracy.