Enhancing Tram Route Optimization with MATLAB: A Client Success Story

From August 2022 to January 2023, I worked on optimizing a tram route for a client in the UK. As a domain expert from MATLAB Helper, I aimed to improve tram efficiency and cut costs using machine learning and data analysis.

The project began with virtual meetings and emails. I gathered the client’s needs and understood the tram route challenges. The initial phase involved collecting data on velocities, gradients, and journey times. Regular sessions ensured this data was fit for training our models.

Using MATLAB, I developed a model. It incorporated route gradients, vehicle speed, and journey times. A key challenge was downhill gradients. They affected speed and coasting efficiency. I applied machine learning to create a model that adjusted to different conditions. It provided optimal route suggestions.

The project needed iterative testing and refinement. During this, challenges arose. There were data inconsistencies and MATLAB code issues. For example, journey times were sometimes unmet at stations. I debugged the code to highlight these stations. Interpolation techniques estimated the required values.

A significant aspect was using MATLAB App Designer. I built a customized application for the client. This application allowed easier interaction with the model. The client could input data, run simulations, and view results. The App Designer’s features were crucial to meeting the client’s needs.

Throughout the project, I discussed various algorithms. These included regression models and decision trees. They helped predict and adjust tram routes dynamically. This ensured better efficiency and lower costs.

The client was working on a PhD thesis. Our help was crucial for their research. By providing robust models, we aided their academic progress. This reinforced our commitment to their success.

Towards the end of January 2023, personal duties called. I had to step away from the project. Another expert from my team took over. The project scope has since grown. This shows our work’s value to the client. The transition was smooth. The client received ongoing support.

By the end of my involvement, the model greatly impacted the client’s strategy. It improved route efficiency and reduced costs. The client was pleased with the results. The project transformed their operations.

This project shows the power of collaboration. It highlights the use of advanced machine learning to solve real problems. As a domain expert, I am proud of this project’s success. It added value to the client’s operations. This experience boosted my skills in MATLAB and machine learning. It also emphasized the importance of client-focused solutions in driving innovation.