Pedro Camargo
Director at PHYLA.earth
Brisbane City, Queensland, Australia
Summary
👤
Senior🎓
Top SchoolPedro Camargo is a transportation scientist and software-driven director with a PhD in Transportation Sciences and more than 15 years building transport models, GIS tools, and algorithms across Brazil, the US and Australia. He leads open-source innovation as the creator of AequilibraE—the first Python-based transport modeling package and QGIS plugin—and contributes data-engineering tooling that bridges TNTP, OMX and Aequilibrae formats. At Outer Loop Consulting and as a contractor for Argonne’s Polaris team he blends hands-on software engineering with discrete choice econometrics and simulation-optimization to turn passive data into actionable supply models. Comfortable moving between academia, consultancy and lab environments, he has shaped national freight planning and metropolitan modeling efforts while mentoring interdisciplinary teams. Based in Brisbane, he is an open-data and open-source advocate who treats pragmatic software development as the most effective way to scale rigorous transport science.
10 years of coding experience
10 years of employment as a software developer
Master Transportation planning and operation, Master Transportation planning and operation at USP - Universidade de São PauloUniversity of California, Irvine
Portuguese, English
Stackoverflow
583reputation
40kreached
9answers
8questions
Github Skills (17)
python10
pandas10
data-parsing10
numpy10
data-transformation10
fileio9
file-access9
file-handling9
file-processing9
shapefile6
pycharm6
dictionary6
docstrings6
environment-variables6
unit-testing6
Programming languages (10)
Github contributions (5)
bstabler/TransportationNetworks
Aug 2017 - Feb 2020
Transportation Networks for Research
Role in this project:
Data Engineer
Data EngineerContributions:12 commits, 5 PRs, 18 comments in 2 years 5 months
Contributions summary:Pedro primarily focused on parsing and processing transportation network data, specifically TNTP format files. Their work involved writing Python code to import, transform, and prepare network and demand data for use within transportation models. The commits demonstrate the creation of functions to handle data from different network formats, and the integration of data into formats compatible with other tools like OMX and Aequilibrae.
transportationnetworksdatasetmachine-learningtraffic
pedrocamargo/rasterstats
Aug 2015 - Oct 2019
Contributions:6 releases, 25 commits, 8 PRs in 4 years 2 months
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