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# Austin Bikeshare data analysis
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As a part of Stream Processing and Stream Analytics project.
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### Project contributers
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- [Mr Philosopher](https://github.com/7ze)
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- [Usama Tahir](https://github.com/Usama00004)
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> **Project Disclaimer**
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>Please note that this project is currently under active development. The
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>information provided in this readme and associated documentation is subject to
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>change without prior notice. While we strive to provide accurate and
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>up-to-date details, certain aspects of the project may be modified, added, or
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>removed as development progresses.
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### Analysis Summary
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---
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This project aims to conduct comprehensive data analysis on Austin Bikeshare
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trips using real-time streaming data. Through advanced data transformations and
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insights generation, this analysis seeks to uncover valuable patterns and
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trends that can inform decision-making and operational improvements within the
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bikeshare system.
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#### Analysis Ideas
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1. *Subscriber Type Analysis:*
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- Explore the distribution of subscriber types (e.g., Pay-as-you-ride,
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Local365).
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- Identify trends in usage patterns based on different subscriber
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categories.
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2. *Popular Stations and Routes:*
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- Determine the most frequently used start and end stations.
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- Uncover common routes taken by bikeshare users for optimization.
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3. *Duration Analysis:*
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- Analyze trip durations to understand average length and identify
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exceptional cases.
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- Explore correlations between trip duration and other factors.
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4. *Bike Usage Analysis:*
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- Investigate bike usage patterns to identify popular bikes and maintenance
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trends.
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- Examine the relationship between bike type and usage frequency.
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5. *Time-based Patterns:*
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- Study usage patterns based on time of day, weekdays vs. weekends, and
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months.
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- Identify peak usage hours and seasonal trends.
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6. *Subscriber Behavior:*
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- Analyze how subscriber types influence trip behaviors such as trip
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duration and station choices.
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- Determine if different subscriber types have distinct usage patterns.
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7. *Station Popularity Over Time:*
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- Track popularity changes of specific stations over time.
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- Investigate factors influencing shifts in station popularity.
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8. *Subscriber Retention Analysis:*
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- Explore whether different subscriber types exhibit varying retention
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rates.
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- Understand which types of users tend to continue using the service over
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time.
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9. *Bike Availability:*
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- Investigate bike availability at stations and its impact on user behavior.
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- Analyze how bike availability affects station popularity.
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10. *Subscriber Demographics (If Available):*
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- Correlate subscriber types with demographic information if available.
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- Understand if certain types of users are more likely to use the service.
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11. *Ride Patterns by Day of the Week:*
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- Observe how usage patterns change across different days of the week.
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- Determine if certain days have higher usage due to specific events or
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reasons.
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#### Final Dataset Schema idea
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```plaintext
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Table: analysis
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Fields:
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- trip_id: STRING (Primary Key)
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- subscriber_type: STRING
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- bike_id: STRING
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- bike_type: STRING
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- start_time: TIMESTAMP
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- start_station_id: INTEGER
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- start_station_name: STRING
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- end_station_id: INTEGER
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- end_station_name: STRING
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- duration_minutes: INTEGER
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- day_of_week: INTEGER
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- hour_of_day: INTEGER
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- month: INTEGER
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- route: STRING
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- is_weekend: BOOLEAN
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- is_peak_hour: BOOLEAN
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- subscriber_demographics: STRING
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- subscriber_retention: BOOLEAN
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- bike_availability: INTEGER
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- station_popularity: INTEGER
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- ...
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Indexes:
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- trip_id (Primary Key) ``` |
