This article examines public transport systems in Budapest, Berlin, Stockholm, and Toronto using GTFS data and data science tools to analyze and visualize public transport patterns and insights for urban planning. The author addresses GTFS’s universality, noting city-specific manual validations, and explores topics like stop locations, departure times, spatial distributions, transport modes, and route shapes with technical implementations and visualizations.
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Streamlining Public Transport Data Analysis with AI
Unlock the potential of public transport data with our easy-to-follow guide on leveraging General Transit Feed Specification (GTFS) data and spatial data science tools. This practical tutorial is designed for middle managers looking to enhance transport, urban planning, and location intelligence through data-driven insights.
Key Takeaways:
- Access and Visualize Data: Learn how to collect, parse, and visualize public transport stop locations using tools like Pandas and GeoPandas.
- Understand Departure Times: Analyze departure time patterns to gain insights into public demand and schedules.
- Spatial Distribution: Utilize spatial indexing and Uber’s H3 hexagon grid to efficiently aggregate data and reveal transport trends.
- Identify Transport Modes: Differentiate between various modes of transport and understand their distribution across cities.
- Analyze Route Shapes: Explore the geographical layout of transport networks and their connectivity.
Our analysis covers four major cities: Budapest, Berlin, Stockholm, and Toronto, highlighting the universality and flexibility of GTFS data. Despite some city-specific nuances, our approach provides a scalable framework for analyzing public transport systems.
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