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

arXiv: https://arxiv.org/abs/2502.01680 · HTML: https://mlsanity.com/paper/2502.01680/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 2 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic](https://mlsanity.com/paper/2008.12238/) (2020)
- [DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction](https://mlsanity.com/paper/2009.00096/) (2020)
- [Predicting Demand for Air Taxi Urban Aviation Services using Machine Learning Algorithms](https://mlsanity.com/paper/2103.14604/) (2021)
- [Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm](https://mlsanity.com/paper/2408.00790/) (2024)
- [Demand Modeling for Advanced Air Mobility](https://mlsanity.com/paper/2412.06807/) (2024)

## Built on by (most cited first, 1 of 1)

- [Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey](https://mlsanity.com/paper/2508.07163/) (2025)

## Abstract (arXiv)

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), (R2), 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.