Graph Path Planning
- Category: Deep Learning
- Project date: 01 May 2024
Graph Path Planning
Developed an optimization algorithm for route planning in a graph structure, specifically tailored for cyclists. The graph representation of Medellin, Colombia, was constructed using OpenStreetMap data. The problem, formulated as a non-convex binary optimization problem with flow conservation, aimed to minimize the path distance while satisfying custom constraints, such as visiting multiple destinations and optimizing for factors like distance and terrain elevation. Future work could focus on several key areas to enhance the current algorithm: Environmental and Health Metrics by including parameters like pollution levels and scenic route option, Integration with Real-Time Data by Incorporating live traffic updates and road conditions could significantly improve the accuracy and utility of the routing recommendations for cyclists.