# Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires

arXiv: https://arxiv.org/abs/2304.06233 · HTML: https://mlsanity.com/paper/2304.06233/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 113 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Activation Functions: Comparison of trends in Practice and Research for Deep Learning](https://mlsanity.com/paper/1811.03378/) (2018)
- [Ablation Studies in Artificial Neural Networks](https://mlsanity.com/paper/1901.08644/) (2019)
- [A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation](https://mlsanity.com/paper/2202.12505/) (2022)
- [Travel Demand Forecasting: A Fair AI Approach](https://mlsanity.com/paper/2303.01692/) (2023)
- [Real-Time Forecasting of Dockless Scooter-Sharing Demand: A Spatio-Temporal Multi-Graph Transformer Approach](https://mlsanity.com/paper/2111.01355/) (2021)
- [Extracting Trips from Multi-Sourced Data for Mobility Pattern Analysis: An App-Based Data Example](https://mlsanity.com/paper/1912.01835/) (2019)
- [Estimating Wildfire Evacuation Decision and Departure Timing Using Large-Scale GPS Data](https://mlsanity.com/paper/2109.07745/) (2021)
- [Forecasting People's Needs in Hurricane Events from Social Network](https://mlsanity.com/paper/1811.04577/) (2018)
- [Simulation Pipeline for Traffic Evacuation in Urban Areas and Emergency Traffic Management Policy Improvements through Case Studies](https://mlsanity.com/paper/2002.06198/) (2020)
- [Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning](https://mlsanity.com/paper/2209.07980/) (2022)

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

- [Harnessing Rich Multi-Modal Data for Spatial-Temporal Homophily-Embedded Graph Learning Across Domains and Localities](https://mlsanity.com/paper/2512.11178/) (2025)

## Abstract (arXiv)

Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. However, few studies focus on accurate travel demand forecasting in large-scale emergency evacuations. Therefore, this study develops and tests a new methodological framework for modeling trip generation in wildfire evacuations by using (a) large-scale GPS data generated by mobile devices and (b) state-of-the-art AI technologies. The proposed methodology aims at forecasting evacuation trips and other types of trips. Based on the travel demand inferred from the GPS data, we develop a new deep learning model, i.e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations. The proposed methodological framework is tested in this study for a real-world case study: the 2019 Kincade Fire in Sonoma County, CA. The results show that SA-MGCRN significantly outperforms all the selected state-of-the-art benchmarks in terms of prediction performance. Our finding suggests that the most important model components of SA-MGCRN are evacuation order/warning information, proximity to fire, and population change, which are consistent with behavioral theories and empirical findings.