# Graph Neural Networks for Travel Distance Estimation and Route Recommendation Under Probabilistic Hazards

arXiv: https://arxiv.org/abs/2501.09803 · HTML: https://mlsanity.com/paper/2501.09803/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Towards Deeper Graph Neural Networks](https://mlsanity.com/paper/2007.09296/) (2020)
- [End-to-End Heterogeneous Graph Neural Networks for Traffic Assignment](https://mlsanity.com/paper/2310.13193/) (2023)
- [Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems](https://mlsanity.com/paper/2210.06404/) (2022)

## Built on by (most cited first, 2 of 2)

- [DSETA: A Dual-Stage Continual Learning Framework for Travel Time Prediction in Dynamic Traffic Environments](https://mlsanity.com/paper/2608.00402/) (2026)
- [GPEvac: GNN-Based PPO for Adaptive Evacuation Routing During Shooting Events](https://mlsanity.com/paper/2609.16163/) (2026)

## Abstract (arXiv)

Estimating the shortest travel time and providing route recommendation between different locations in a city or region can quantitatively measure the conditions of the transportation network during or after extreme events. One common approach is to use Dijkstra's Algorithm, which produces the shortest path as well as the shortest distance. However, this option is computationally expensive when applied to large-scale networks. This paper proposes a novel fast framework based on graph neural networks (GNNs) which approximate the single-source shortest distance between pairs of locations, and predict the single-source shortest path subsequently. We conduct multiple experiments on synthetic graphs of different size to demonstrate the feasibility and computational efficiency of the proposed model. In real-world case studies, we also applied the proposed method of flood risk analysis of coastal urban areas to calculate delays in evacuation to public shelters during hurricanes. The results indicate the accuracy and computational efficiency of the GNN model, and its potential for effective implementation in emergency planning and management.