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Robo-Uber Project: Building an Automated Taxi Dispatch System

Dive into the Robo-Uber project, a simplified system for an automated taxi service. Learn about planning, bidding, and dispatching strategies for optimal taxi service in a simulated environment.

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<h3><span style="font-size: 14pt;">Robo-Uber Project: Building an Automated Taxi Dispatch System</span></h3>
<p><span style="font-size: 14pt;"><strong>Project Goal</strong>: Develop a simplified, intelligent system for an automated taxi service involving both taxi agents and a central dispatcher, focusing on optimization, planning, and bid-based dispatching.</span></p>
<hr>
<h3><span style="font-size: 14pt;">Project Overview</span></h3>
<p><span style="font-size: 14pt;">In Robo-Uber, you’ll develop a multi-agent system where both taxis and a central dispatcher work collaboratively to transport users (‘fares’) efficiently. Taxis operate on a pre-set map, navigate around obstacles, and are responsible for bidding on fares while a dispatcher manages fare allocations and maximizes system returns.</span></p>
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<h4><span style="font-size: 14pt;">System Components</span></h4>
<ol>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Service Area</strong>: A networked map of roads with random fare requests.</span></li>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Central Dispatcher</strong>: Handles fare requests, bidding, and fare assignments.</span></li>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Taxi Agents</strong>: Simulated taxis that handle route planning, bidding, and earning management.</span></li>
</ol>
<p><span style="font-size: 14pt;">Time in this simulation advances in one-second intervals, and traffic conditions, road capacity, and fare requests vary.</span></p>
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<h3><span style="font-size: 14pt;">Project Tasks</span></h3>
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<h4><span style="font-size: 14pt;"><strong>Task 1: Path Planning (20%)</strong></span></h4>
<ol>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Initial Evaluation</strong>:</span>
<ul>
<li style="font-size: 14pt;"><span style="font-size: 14pt;">Run a basic system simulation and analyze taxi and dispatcher returns, operational uptime, and fare cancellations.</span></li>
</ul>
</li>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Path Planning Enhancement</strong>:</span>
<ul>
<li style="font-size: 14pt;"><span style="font-size: 14pt;">Modify the <code>_planPath</code> function to optimize route efficiency. Justify your approach and compare results with the baseline.</span></li>
</ul>
</li>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Probabilistic Path Planning</strong> (Optional):</span>
<ul>
<li style="font-size: 14pt;"><span style="font-size: 14pt;">Implement a path planner considering real-time traffic estimates and analyze its impact on fare cancellations.</span></li>
</ul>
</li>
</ol>
<hr>
<h4><span style="font-size: 14pt;"><strong>Task 2: Dispatcher Optimization (10%)</strong></span></h4>
<ul>
<li style="font-size: 14pt;"><span style="font-size: 14pt;">Enhance the <code>_allocateFare</code> function to maximize returns by optimizing fare scheduling, reducing wait times, and balancing bid requirements.</span></li>
<li style="font-size: 14pt;"><span style="font-size: 14pt;">Perform a comparative analysis of schedules and returns before and after changes.</span></li>
</ul>
<hr>
<h4><span style="font-size: 14pt;"><strong>Task 3: Taxi Bidding Strategy (20%)</strong></span></h4>
<ol>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Bid Optimization</strong>:</span>
<ul>
<li style="font-size: 14pt;"><span style="font-size: 14pt;">Update the <code>_bidOnFare</code> function to improve expected returns, comparing outcomes with and without traffic.</span></li>
</ul>
</li>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>System Analysis</strong>:</span>
<ul>
<li style="font-size: 14pt;"><span style="font-size: 14pt;">Evaluate the commercial feasibility of this automated taxi service and areas needing further testing.</span></li>
</ul>
</li>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Dispatcher Cost Function</strong> (Optional):</span>
<ul>
<li style="font-size: 14pt;"><span style="font-size: 14pt;">Modify <code>_costFare</code> to maximize fare transport probability, minimize cancellations, and discuss its application in real-world deployments.</span></li>
</ul>
</li>
</ol>
<hr>
<h3><span style="font-size: 14pt;">Assessment Criteria</span></h3>
<p><span style="font-size: 14pt;">Your project will be assessed on:</span></p>
<ul>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Task Functionality</strong>: Meeting basic requirements for each component.</span></li>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Quality and Originality</strong>: Innovative approaches in planning, dispatch, and bidding.</span></li>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Coding Practice and Documentation</strong>: Clean, well-documented code.</span></li>
<li style="font-size: 14pt;"><span style="font-size: 14pt;"><strong>Evaluation and Analysis</strong>: Insightful comparisons and recommendations.</span></li>
</ul>
<p><span style="font-size: 14pt;">This individual project emphasizes foundational AI principles in transport logistics, providing practical experience in route optimization, fair bidding, and dispatch decision-making for real-time applications.</span></p>
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Robo-Uber Project: Building an Automated Taxi Dispatch System

Dive into the Robo-Uber project, a simplified system for an automated taxi service. Learn about planning, bidding, and dispatching strategies for optimal taxi service in a simulated environment.

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