Permanent Bicycle Counter Data With Speed And Aggregated Time Series

Bicycle counting detectors record cyclist crossings per minute, with some locations providing speed data. The raw data is aggregated into 15-minute, hourly, daily, monthly, and annual time series to monitor traffic evolution and seasonal variations. The data is sourced from sensors installed by SUM-DIGD across various sites.

Use Cases

• Analyze seasonal variations in bicycle traffic using aggregated daily and monthly time series.
• Model the impact of specific events or infrastructure developments on cyclist counts from minute-level data.
• Study cyclist behavior patterns by correlating speed data with aggregated hourly counts where available.
• Forecast bicycle traffic trends using consistent annual aggregated time series.

Strengths

• Data is aggregated into multiple consistent time series (15-minute, hourly, daily, monthly, annual) for flexible analysis.
• Some locations include speed data, enabling behavioral analysis beyond simple counts.
• Aggregation uses automated processes to guarantee time series consistency.

Limitations

• The exact number of sensors, rows of data, and geographic scope are not specified.
• Speed data is not available for all counting locations, limiting behavioral analysis to a subset.
• Data description is an automated translation, which may introduce ambiguity.

Provenance

Source

Government and Municipalities of Québec

Collection Method

Recorded by permanent bicycle counting detectors installed by SUM-DIGD.

Time Range

null

Freshness

Last updated March 2026.

Geography

Various sites in Québec (implied).