trafikprofil

A trafikprofil is a data-driven portrait of traffic behavior for a specific area and period. It summarizes typical patterns in traffic flow, including volumes, speeds, densities, and travel times, and may also describe modal split and origin–destination tendencies. Trafikprofiler are produced for various scales, from a city or district to a single corridor or junction, and are updated to reflect changing conditions.

The profile is built from multiple data sources. Traditional traffic counts and loop detectors provide volume and speed data, while cameras, radar, and floating car data add spatial detail. Modern approaches use GPS data from private vehicles, mobile network data, transit ridership, and travel surveys to capture broader patterns. Data are often combined (data fusion) to improve coverage and reliability, with attention given to privacy and aggregation.

Methods used to create and interpret trafikprofiler include statistical summaries, time-series analysis, and calibration of travel demand models. Origin–destination estimation, traffic assignment, and scenario analysis help translate observed patterns into forecasts and plans. Machine learning may be employed to detect recurrent patterns and anomalies.

Outputs of a trafikprofil typically include metrics such as hourly volumes, average and median speeds, travel times, queue lengths, and measures of reliability. They support traffic management decisions (e.g., signal timing, incident response), infrastructure planning (e.g., capacity improvements, new road or transit options), and policy analysis (e.g., demand management, mode shift). Limitations include data gaps, sensor bias, privacy considerations, and uncertainties in forecasting, necessitating regular updates and validation.