# Incorporating graph neural network into route choice model

arXiv: https://arxiv.org/abs/2503.02315 · HTML: https://mlsanity.com/paper/2503.02315/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 68 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [Directed Graph Convolutional Network](https://mlsanity.com/paper/2004.13970/) (2020)
- [Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks](https://mlsanity.com/paper/1803.11115/) (2018)
- [Choice modelling in the age of machine learning -- discussion paper](https://mlsanity.com/paper/2101.11948/) (2021)
- [STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph](https://mlsanity.com/paper/2210.16789/) (2022)
- [A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards](https://mlsanity.com/paper/2206.10598/) (2022)
- [Real-Time Accident Detection in Traffic Surveillance Using Deep Learning](https://mlsanity.com/paper/2208.06461/) (2022)
- [Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling](https://mlsanity.com/paper/1706.00505/) (2017)
- [A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN Prediction](https://mlsanity.com/paper/2009.14627/) (2020)

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

- [Aligning LLM with human travel choices: a persona-based embedding learning approach](https://mlsanity.com/paper/2505.19003/) (2025)

## Abstract (arXiv)

Route choice models are one of the most important foundations for transportation research. Traditionally, theory-based models have been utilized for their great interpretability, such as logit models and Recursive logit models. More recently, machine learning approaches have gained attentions for their better prediction accuracy. In this study, we propose novel hybrid models that integrate the Recursive logit model with Graph Neural Networks (GNNs) to enhance both predictive performance and model interpretability. To the authors' knowldedge, GNNs have not been utilized for route choice modeling, despite their proven effectiveness in capturing road network features and their widespread use in other transportation research areas. We mathematically show that our use of GNN is not only beneficial for enhancing the prediction performance, but also relaxing the Independence of Irrelevant Alternatives property without relying on strong assumptions. This is due to the fact that a specific type of GNN can efficiently capture multiple cross-effect patterns on networks from data. By applying the proposed models to one-day travel trajectory data in Tokyo, we confirmed their higher prediction accuracy compared to the existing models.