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	<title>Tech &amp; AI Insights &#8211; Gunjan Gupta</title>
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	<title>Tech &amp; AI Insights &#8211; Gunjan Gupta</title>
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		<title>Enhancing Tram Route Optimization with MATLAB: A Client Success Story</title>
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		<dc:creator><![CDATA[Gunjan]]></dc:creator>
		<pubDate>Wed, 24 Jul 2024 09:52:19 +0000</pubDate>
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					<description><![CDATA[From August 2022 to January 2023, I worked on optimizing a tram route for a client in the UK. As...]]></description>
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									<p>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.</p><p>The project began with virtual meetings and emails. I gathered the client&#8217;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.</p><p>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.</p><p>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.</p><p>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&#8217;s features were crucial to meeting the client&#8217;s needs.</p><p>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.</p><p>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.</p><p>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&#8217;s value to the client. The transition was smooth. The client received ongoing support.</p><p>By the end of my involvement, the model greatly impacted the client&#8217;s strategy. It improved route efficiency and reduced costs. The client was pleased with the results. The project transformed their operations.</p><p>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&#8217;s success. It added value to the client&#8217;s operations. This experience boosted my skills in MATLAB and machine learning. It also emphasized the importance of client-focused solutions in driving innovation.</p>								</div>
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