# IUTF Dataset: Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation

## TL;DR

The IUTF dataset combines traffic, weather, and road data from 40 cities to study how rainfall affects urban transportation.

## Contribution

The IUTF dataset is the first to integrate traffic, precipitation, and road network data across multiple continents for cross-border analysis.

## Key findings

- The dataset includes 21,739 sensors across 40 cities in Europe, North America, and Asia.
- It harmonizes traffic data with high-resolution precipitation and road network topology.
- The dataset supports traffic prediction and resilience modeling through spatio-temporal analysis.

## Abstract

Understanding the impact of extreme weather, particularly flooding, on urban transportation systems is critical for enhancing city resilience and traffic management. However, research and policy development are often hampered by a lack of datasets that comprehensively integrate detailed traffic dynamics, high-resolution weather information, and road network topology across multiple diverse urban environments. To address this significant gap, we present the Integrated Urban Traffic-Flood (IUTF) dataset. This open-access resource covers 40 major cities across Europe, North America, and Asia, including 21,739 sensors. The IUTF dataset uniquely combines (i) high-resolution traffic parameters derived from over 21,700 sensors (with raw data typically at 5-minute intervals, harmonised to hourly); (ii) detailed hourly precipitation data from ERA5 reanalysis, spatially aligned with (iii) the underlying road network topology for over 1 million road segments, processed from OpenStreetMap. This meticulously curated and validated dataset, created through a novel spatio-temporal harmonisation framework, enables unprecedented, cross-border analysis of weather impacts on urban mobility. It provides a foundational data resource to support applications in traffic flow prediction, infrastructure planning, and the future development of quantitative resilience models.