# Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models

arXiv: https://arxiv.org/abs/2401.13098 · HTML: https://mlsanity.com/paper/2401.13098/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 23 Jan 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning](https://mlsanity.com/paper/2008.12833/) (2020)
- [GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection](https://mlsanity.com/paper/1912.00682/) (2019)
- [Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting](https://mlsanity.com/paper/2310.18948/) (2023)
- [Predicting commuter flows in spatial networks using a radiation model based on temporal ranges](https://mlsanity.com/paper/1410.4849/) (2014)
- [Reconstructing commuters network using machine learning and urban indicators](https://mlsanity.com/paper/1908.03512/) (2019)
- [A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels](https://mlsanity.com/paper/2207.05514/) (2022)
- [Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning](https://mlsanity.com/paper/2202.13867/) (2022)
- [Field theory for recurrent mobility](https://mlsanity.com/paper/1908.11814/) (2019)

## Built on by (most cited first, 5 of 5)

- [Physics-Informed Neural Networks for Vessel Trajectory Prediction: Learning Time-Discretized Kinematic Dynamics via Finite Differences](https://mlsanity.com/paper/2506.12029/) (2025)
- [Transferable Human Mobility Network Reconstruction with neuroGravity](https://mlsanity.com/paper/2604.23678/) (2026)
- [ImPORTance: Machine Learning-Driven Analysis of Global Port Significance and Network Dynamics for Improved Operational Efficiency](https://mlsanity.com/paper/2407.09571/) (2024)
- [Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach](https://mlsanity.com/paper/2506.20359/) (2025)
- [A Systematic Approach for Selecting Trajectories for Data Augmentation](https://mlsanity.com/paper/2606.10938/) (2026)

## Abstract (arXiv)

Aquatic non-indigenous species (NIS) pose significant threats to biodiversity, disrupting ecosystems and inflicting substantial economic damages across agriculture, forestry, and fisheries. Due to the fast growth of global trade and transportation networks, NIS has been introduced and spread unintentionally in new environments. This study develops a new physics-informed model to forecast maritime shipping traffic between port regions worldwide. The predicted information provided by these models, in turn, is used as input for risk assessment of NIS spread through transportation networks to evaluate the capability of our solution. Inspired by the gravity model for international trades, our model considers various factors that influence the likelihood and impact of vessel activities, such as shipping flux density, distance between ports, trade flow, and centrality measures of transportation hubs. Accordingly, this paper introduces transformers to gravity models to rebuild the short- and long-term dependencies that make the risk analysis feasible. Thus, we introduce a physics-inspired framework that achieves an 89% binary accuracy for existing and non-existing trajectories and an 84.8% accuracy for the number of vessels flowing between key port areas, representing more than 10% improvement over the traditional deep-gravity model. Along these lines, this research contributes to a better understanding of NIS risk assessment. It allows policymakers, conservationists, and stakeholders to prioritize management actions by identifying high-risk invasion pathways. Besides, our model is versatile and can include new data sources, making it suitable for assessing international vessel traffic flow in a changing global landscape.