High-resolution traffic flow data from the urban traffic control system in Glasgow

Area of Science:

• Urban Planning
• Transportation Science
• Geographic Information Science

Background:

• Existing traffic flow datasets suffer from low spatiotemporal resolution and inconsistent quality.
• Data collection methods and cleaning processes often limit the utility of current traffic data.
• There is a need for detailed, long-term traffic flow data for comprehensive urban analysis.

Purpose of the Study:

• To introduce a novel, high-spatiotemporal-granularity, long-term traffic flow dataset.
• To cover the Glasgow City Council area over four years, including the COVID-19 pandemic period (October 2019 - September 2023).
• To facilitate diverse applications in traffic analysis, management, and urban planning.

Main Methods:

• Collection and processing of intra-city traffic flow data.
• Ensuring high spatiotemporal resolution and data quality over a four-year period.
• Dataset spans October 2019 to September 2023 for Glasgow City Council area.

Main Results:

• A comprehensive, long-term traffic flow dataset with high granularity has been generated.
• The dataset provides detailed temporal and spatial coverage of urban traffic.
• It captures traffic dynamics across a significant period, including the COVID-19 pandemic.

Conclusions:

• The new dataset supports advanced traffic dynamic analysis and management.
• It is valuable for infrastructure planning and urban environment improvement.
• The dataset offers a unique resource for studying pandemic-induced traffic changes.