# Predicting commuter flows in spatial networks using a radiation model based on temporal ranges

arXiv: https://arxiv.org/abs/1410.4849 · HTML: https://mlsanity.com/paper/1410.4849/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2014.

## Built on by (most cited first, 9 of 9)

- [Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks](https://mlsanity.com/paper/2101.00307/) (2021)
- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)
- [Harnessing LLMs for Cross-City OD Flow Prediction](https://mlsanity.com/paper/2409.03937/) (2024)
- [Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models](https://mlsanity.com/paper/2401.13098/) (2024)
- [Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities](https://mlsanity.com/paper/2110.12371/) (2021)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics*](https://mlsanity.com/paper/2206.01176/) (2022)
- [Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network](https://mlsanity.com/paper/2405.14079/) (2024)
- [VisitHGNN: Heterogeneous Graph Neural Networks for Modeling Point-of-Interest Visit Patterns](https://mlsanity.com/paper/2510.02702/) (2025)

## Abstract (arXiv)

Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and of human mobility. Here we show a first-principles based method for traffic prediction using a cost based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the lognormal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared to real traffic. Due to its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.