A City-Scale Dataset of Traffic Flows, Travel Times, and Urban Context
Abstract
We present a multi-source traffic dataset derived from Automatic Vehicle Identification (AVI) recordings in Padua, Italy, spanning from February 2026 to August 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 Interest (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.
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BibTeX
@misc{cappi2026cityscale,
title = {{A City-Scale Dataset of Traffic Flows, Travel Times, and Urban Context}},
author = {Riccardo Cappi and Massimiliano Luca and Pietro Fontolan and Nicolò Navarin and Bruno Lepri and Alessandro Sperduti},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.18782},
eprint = {2605.18782},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.18782},
}
RIS
TY - UNPB
TI - A City-Scale Dataset of Traffic Flows, Travel Times, and Urban Context
AU - Riccardo Cappi
AU - Massimiliano Luca
AU - Pietro Fontolan
AU - Nicolò Navarin
AU - Bruno Lepri
AU - Alessandro Sperduti
PY - 2026
DA - 2026/09/08
PB - arXiv
DO - 10.48550/arxiv.2605.18782
UR - https://arxiv.org/abs/2605.18782
AB - We present a multi-source traffic dataset derived from Automatic Vehicle Identification (AVI) recordings in Padua, Italy, spanning from February 2026 to August 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 Interest (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.
ER -