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In about six months, we created an advanced proof-of-concept using a deep-learning model to recognize different transport modes. The project required expertise in machine learning, backend development, mobile development, blockchain, GIS, and scientific research. The team's experience and client’s trust were vital for success. The main challenge was coordinating tasks to start early model training and experimentation with different approaches, demanding discipline from the entire team due to the direct impact on machine learning results.


The solution

The cloud platform was ready and capable of serving requests from the mobile application. 
The machine learning transport mode recognition service was in place and waiting to run inference against acquired sensor data.


The results

***** *****

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Learn how SoftwareMill worked with Codos Foundation and how they can support your Non-Profit & Philanthropy product and team.

SoftwareMill Success Story in Non-Profit & Philanthropy for Codos Foundation - A case study | 2024

Reasonfield Lab by SoftwareMill was to create an end-to-end solution for the Codos Foundation, which involved detecting transportation modes based on data from mobile phones. The goal was to calculate CO2 emission avoidance to reward users for sustainable commuting. The project required high precision in machine learning models and handling continuous mobile sensor data. Understanding the spatial context was crucial since user motion occurs in cities, making the task a challenging combination of science and engineering.


The challenge

The client stakeholders at Codos Foundation were working closely with the team at SoftwareMill

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End-to-End transportation mode recognition software

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