A model of Visayas’ transport network system, showing connections between major roads (red lines), sea routes (blue lines), seaports (blue dots), and food hubs (green icons). (Photo credit: JCA. C. Peralta)

UPD-CS Science Communications

UP scientists are bringing an almost 300-year-old math discipline to life-saving relevance in the 21st Century, paving the way for better and more efficient relief operations in response to typhoons.

First formally described in the early 1700s by the Swiss mathematician and physicist Leonhard Euler, the discipline now known as “network science” was earlier applied to the problem of figuring out the most efficient way to visit a set of destinations—the so-called “traveling salesman” problem.

Using network science for disaster relief

Now, as UP scientists have shown, this powerful branch of mathematics can be used to improve disaster relief efforts after a typhoon. In network science, lines that connect two points are called edges, while the points at which the lines intersect are called nodes. Together, edges and nodes make up a graph, which can be applied to a variety of modeling applications including disaster relief.

Dr. May T. Lim and Dr. Reinabelle C. Reyes of the UP Diliman National Institute of Physics (UP-CS NIP), with independent researcher JC Albert C. Peralta, used these concepts to model transport networks: by representing roads and sea routes as edges and the intersections between roads and sea routes as nodes, they were able to calculate the best ways to distribute relief goods.

To simulate how relief operations move through different towns and districts, the researchers first pinpointed the food hubs from where relief packages are first received. They then simulated the relief packages moving outwards from these hubs to the edges and nodes, like water flowing through a network of pipes.  As part of their model, they also assigned each edge with a travel time, or the time it takes for a vehicle to traverse the road segment or sea route. This allowed them to calculate how long it would take for relief to reach different destinations.

Visayas: Proof of concept

The researchers tested their model on the Visayas region using geographic data from Google Maps, OpenStreetMap, and other publicly-accessible sources. Their model showed that Region 8, particularly in Northern and Eastern Samar, is the most vulnerable to relief delivery delays, taking as much as 12 hours for relief packages to arrive from the food hubs to these districts.

Relief delivery delays in Visayas transport network system when no connections are damaged. Region 8 is the most vulnerable to delays. (Photo credit: JCA. C. Peralta)

The researchers then simulated how typhoon damages affect relief operations, by randomly removed nodes to mimic impassable roads. Their simulation of the Visayas transport network revealed the extent of its vulnerability: when even just 1% of the total nodes became inaccessible, 30 out of 251 towns became disconnected from the main network. More worryingly, almost all towns in the region became inaccessible when only just 5% of all nodes were shut down.

“We emphasize the need for a more decentralized and proactive form of relief logistics such as prepositioning relief goods, especially in or nearer to towns most vulnerable to disconnection,” the researchers cautioned.

Nationwide applicability

Although the model was only tested on the Visayas transport network system, the researchers underscored its potential application to all network systems in the Philippines. To showcase this usefulness, they made a prototype app that offers a user-friendly approach for the public. Currently, only the Visayas network system is available, but more transport networks are expected to be available as more data is brought into the model. They also said that the model’s accuracy and usefulness could be improved by using data based on actual rather than hypothetical damage.

The UP physicists’ work demonstrates how network science can aid in disaster response and preparedness. “It is our hope that policymakers will harness this potential for informed decision-making, strategic cost-benefit analysis for infrastructure investments, and effective data-driven transportation planning to enhance resilience in the face of future disasters,” the researchers concluded.