Using historical data from the City Of Winnipeg, this repository performs exploratory analysis to identify inefficiencies in the city's transit system with regards to scheduled times & leverage these findings to then design a real-time bus tardiness prediction system prototype for individual bus stops.
- 🔢 Transit Delays - Primary data from City of Winnipeg
- 🔢 Road Network - Auxiliary data from Government of Canada to enrich bus stop info.
- 🔢 POIs - Auxiliary data from Open Street Map to proxy as normalization of areas by type & density
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Historical Weather Conditions- Auxiliary data to contextualize current driving conditions. - 🔢
Traffic / Incidents / Construction- Auxiliary data to contextualize known delays. - 🛠️ QGIS Editor - Mapping tool for exploration & data processing
- Part1: Insights from the dataset
- Part2: Design delay prediction solutions
- Part3: Implement prototype system
pip install -r requirements.txt; # download project external libraries
env PYTHONPATH=${PWD} jupyter lab; # launch interactive notebook environment