Opportunity brief
Data Tactix challenges you to design an AI-powered emergency response solution for a fictional city. Combining data analytics, machine learning, graph optimization and strategic thinking, your team will build a traffic-aware navigation system and recommend how the city can implement and operate it.
Your client is a fictional city government seeking to improve emergency response in high-traffic areas.Your solution must address three key objectives:
- Analyse Traffic: Explore and analyse the supplied synthetic traffic data.
- Optimize Routes: Develop a traffic-aware route planner using A*, Dijkstra or another justified algorithm.
- Build a Decision Dashboard: Develop an interactive dashboard that enables the city to visualize congestion, compare alternative routes, understand travel-cost trade-offs and evaluate the performance of your proposed solution.
Competition Format
- Duration: 48-hour data-thon
- Format: Technical challenge + consulting case
- Tools: Python, data analytics, machine learning and graph optimization
- Dataset: Synthetic traffic data
Deliverables
Each team must submit the following on Unstop:
- Source Code: Reproducible code with setup and execution instructions.
- Decision Dashboard: An interactive dashboard displaying congestion patterns, route visualizations, distance and predicted travel-cost comparisons, and relevant performance metrics.
Rules and Guidelines
- Use the supplied datasets and resources as the foundation of your solution
- Document your modelling choices, routing cost function, blocking policy and assumptions.
- This is a fictional educational simulation. The supplied city, traffic labels and costs do not represent validated real-world emergency-routing or safety data.