Driving Decarbonization: Automating Student and Employee Travel Time and Distance Calculations for the University of Colorado Boulder

Project: Origin-Destination (OD) Cost Analysis Automation Tool

Client: University of Colorado Boulder (UCB) Sustainable Transportation Team

The University of Colorado Boulder is currently undertaking a campus-wide decarbonization initiative. A key component of this effort involves measuring and reducing Scope 3 emissions, specifically those generated by student and employee commutes. To support this planning, UCB requires accurate travel time and mileage calculations for all local affiliates, an address dataset comprising over 52,000 records.

The Technical Challenge

UCB has been utilizing the Esri OD (Origin-Destination) Cost Matrix tool within ArcGIS Pro to calculate travel metrics. This tool uses the ArcGIS Online World Route Service, which has a limit of 1,000 address points per request.

With the dataset containing over 15,000 employee records and over 37,000 student records, the standard manual workflow was inefficient. Processing the data required:

• Manually splitting the master dataset into 50+ separate batch files.

• Running the OD Cost Matrix tool individually for each batch.

• Manually merging the outputs into a final dataset.

Performing these manual tasks was not only time-intensive but also susceptible to error, and the output required manual validation.

Estimates indicated that processing the employee records alone required roughly 25-30 hours. Processing the full dataset (students and employees) manually was estimated to require 35-40 hours of labor, with annual projections of 40-60 hours of total processing time.

The Solutions: Python Batch Processing

To address this challenge, the UCB Sustainable Transportation team and the Planning, Design, and Construction office engaged Cloudpoint Geospatial to develop a custom Python script to batch automate this workflow. The script automates the process by splitting the input data into batches (chunks) and processing each chunk in turn. It then joins and merges the results from each batch into the master output table.

The solution functions as follows:

1. The script accepts large datasets and automatically splits them into batches of 1,000 features.

2. It iterates through each batch, running the Esri OD Cost Matrix tool in the background.

3. Once processing is complete, the script merges the results into a single, comprehensive dataset with both travel time (minutes) and distance (miles).

Performance Outcomes

The automation script successfully processed the combined employee and student datasets in 47 minutes.

This result compares with an estimated manual processing time of 35 hours, representing a 98% reduction.

By streamlining the data processing workflow, UCB can now update its transportation metrics within minutes, allowing the Sustainable Transportation team to run the tool more often and spend more time analyzing the results rather than on manual processing.

If you have a data challenge that could be solved or streamlined by automation, get in touch! Time savings alone generate a return on investment, while ensuring quality results and freeing your staff to focus on analysis and decision-making rather than data processing.

To learn more about Cloudpoint's GIS services, please contact us!