Problem
Research questions and friction points this paper is trying to address.
Traffic Flows
Travel Times
Urban Context
Automatic Vehicle Identification
Points Of Interest
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Automatic Vehicle Identification (AVI)
spatio-temporal graph representation
traffic flow statistics
urban contextual information
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