Data Exchange

Data Collection

1. Identify Data Sources for Public Transit

• GPS Systems:
  • GPS devices installed in buses or trains continuously transmit real-time location data.
  • Transit agencies may provide API endpoints or data feeds for accessing this information.
• Management Systems:
  • Depot management systems track vehicle assignments, maintenance schedules, and operational data.
  • Operations management systems record daily revenue, operational statistics, and route adherence.
• Manual Inputs:
  • Data on revenue, ridership, or one-off operational changes may be collected through spreadsheets or forms manually filled by transit personnel.

2. Develop Data Collection Templates

To standardize input data across multiple sources, create templates tailored to specific categories of data.

Schedule Data Template:

route_id	stop_sequence	stop_id	arrival_time	departure_time
101	1	1001	08:00:00	08:05:00
102	2	1002	08:10:00	08:15:00

Vehicle Data Template:

vehicle_id	fuel_type	depot	agency
V001	Diesel	Central Depot	DMRC
V002	Electric	West Depot	DTC

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Data Cleaning and Processing

Once data is collected, cleaning and preprocessing are crucial to ensure that the datasets are accurate, complete, and ready for transformation.

1. Remove Invalid or Redundant Data

• Eliminate duplicate entries in critical fields such as stop_id or route_id.
• Identify and flag missing or erroneous data:
  • stop_lat and stop_lon values outside valid geographic bounds.
  • Overlapping or conflicting schedules.

2. Data Validation Rules

• Location Data: Ensure stop_lat and stop_lon values are within the service area and adhere to precision requirements (e.g., up to 4 decimal places).
• Schedule Data: Validate that arrival_time is earlier than departure_time for each stop.
• Relationships: Cross-check dependencies between datasets (e.g., stop_id in schedules matches entries in the stops list).

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Data Transformation Utility

A robust utility for transforming raw data into structured formats ensures compatibility with downstream systems. Using Python and libraries like Pandas, NumPy, or GeoPandas, you can automate and standardize data transformation.

1. Standardizing Location Data

• Clean stop_lat/stop_lon to conform with:
  • Defined bounds of the transit area (latitude: ±90°, longitude: ±180°).
• Remove invalid or outlier coordinates.

2. Eliminating Duplicate Data

• Remove duplicate values across datasets such as route_id or stop_id.

3. Transforming GPS Data

• Convert raw GPS feeds into structured formats like GTFS-RT.

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Testing and Validation

Testing ensures the pipeline delivers clean and actionable data for all use cases.

1. Validation Checks

• Verify that:
  • GPS coordinates match expected routes and stops.
  • All stop and route relationships are preserved post-transformation.

2. End-to-End Pipeline Validation

• Test if data feeds into the pipeline correctly and outputs match the expected structure.
• Cross-check the transformed data with the original input:
  • Do stop locations align with city maps?
  • Are schedules and trips correctly generated?

3. Testing Tools

• Use automated scripts for data quality assurance.
• Perform manual checks for geographical alignment and schedule accuracy.