This is the slide for iRun, the running route recommendation system I built for runners.

1. iRun
Yannan Zheng
Ph.D. Candidate, MIT

2. Here is the problem

3. Here is the problem
Sometimes, I want to take a run from Lab to Home

4. Here is the problem
Sometimes, I want to take a run from Lab to Home
But I want to run different miles on different days

5. Here is the problem
Sometimes, I want to take a run from Lab to Home
But I want to run different miles on different days
I want to run different routes on different days

6. Here is the problem
Sometimes, I want to take a run from Lab to Home
But I want to run different miles on different days
I want to run different routes on different days
How to find routes with targeted distances?

7. Building a graph of Cambridge and Boston

8. Data Clean Up 0: Original Map

9. Data Clean Up I: Remove un-runnable area

10. Data Clean Up II: Remove isolated islands

11. Data Clean Up III: Remove spikes

12. Data Clean Up IV: Combine parallel roads

13. Data Clean Up IV: remove redundant nodes

14. After Data Clean Up
103,365 Nodes, 225,766 edges
17,480 Nodes, 49,354 edges
5min 30s for path search
~5 fold reduction

15. Starting Address: My Lab
Ending Address: My Home
Desired Distance: 3km
Path Finding Algorithm:
My LabMy Home

16. Path Finding Algorithm:
Dijkstra’s Algorithm finding shortest path
Shortest Path Length 1131m
My LabMy Home

17. Score
• Penalize difference between desired distance
and actual distance
• Penalize turns / loops / zigzags
• Penalize repetitive route

18. Path Finding Algorithm:
Monte Carlo Heuristic perturbation of route
penalty score = 4.99
path length = 1131m
My LabMy Home

19. Path Finding Algorithm: Monte Carlo
accept improvements
penalty score = 2.95
path length = 1875m
My LabMy Home

20. Path Finding Algorithm: Monte Carlo
only accept deterioration with low probability
penalty score = 39.32
path length = 8064m
My LabMy Home

21. Path Finding Algorithm: Monte Carlo
best route after 1000 iterations
penalty score = 0.495
path length = 2985m
My LabMy Home

22. Path Finding Algorithm: Monte Carlo
best route after another 1000 iterations
penalty score = 0.251
path length = 3202m
My LabMy Home

25. Summary
• Build a route recommendation system for runners
• Recommend different routes for given running
distances
• Customized running score
• Can be easily generalized for other purposes

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About Me

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About Me

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About Me

29. Thank You!