A City-Scale Dataset of Traffic Flows, Travel Times, and Urban Context

TL;DR

This paper introduces a comprehensive, multi-source city-scale traffic dataset from Padua, Italy, combining traffic flows, travel times, and urban context, validated by typical traffic pattern analysis.

Contribution

It provides a novel, integrated dataset combining traffic data with urban context, accessible via a Python class for spatio-temporal analysis.

Findings

Dataset captures typical rush hour traffic patterns.

Includes diverse data sources like POIs, demographics, and weather.

Validated to reflect expected weekday and weekend traffic routines.

Abstract

We present a multi-source traffic dataset derived from Automatic Vehicle Identification (AVI) recordings in Padua, Italy, spanning from February 2026 to April 2026. The dataset combines traffic volume time series, aggregated at 10-minute intervals, with time-varying trajectory-based flow statistics including transition probability matrices, average travel times, and flow residuals. To enrich the traffic measurements with urban contextual information, we integrate Points Of Interests (POIs), demographic data, meteorological variables, and road infrastructure data. All components are accessible through a Python class that loads temporal and contextual data exploiting a spatio-temporal graph representation. Validation analyses confirm that the dataset captures expected traffic patterns, such as morning and evening rush hours, as well as weekdays vs. weekend days traffic routines.