MAtlas: A case study on Milan mobility

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MAtlas: A case study on Milan mobility

  1. 1. MAtlas : a case study on Milano, Italy
  2. 2. Dataset info <ul><li>GPS traces </li></ul><ul><li>17K private cars </li></ul><ul><li>one week of ordinary mobility </li></ul><ul><li>200K trips (trajectories) </li></ul><ul><li>Milan, Italy </li></ul><ul><li>Data donated by </li></ul><ul><li> OCTO Telematics Italia </li></ul>
  3. 3. <ul><li>Overall view of trips performed in a single day (Wednesday, April 4 th , 2007) </li></ul><ul><ul><li>Difficult to understand anything </li></ul></ul>
  4. 4. <ul><li>Temporal analysis: intensity of traffic (n. of moving vehicles) per hour over the week </li></ul><ul><ul><li>The same double-peeked shape for all days, a bit lower in the weekends </li></ul></ul>
  5. 5. <ul><li>Distribution of lengths of the trips </li></ul><ul><ul><li>Neat power-law -> several short trips, few very long ones </li></ul></ul>
  6. 6. <ul><li>Distribution of trip duration </li></ul><ul><ul><li>Another power-law, similar shape </li></ul></ul>
  7. 7. <ul><li>How do length and speed of trips correlate? </li></ul><ul><ul><li>Average length grows with avg. speed (right plot) </li></ul></ul><ul><ul><li>Yet, only slow trips reach considerable length (left) </li></ul></ul>
  8. 8. <ul><li>Where is traffic concentrated between midnight and 2 a.m.? (red = most intense) </li></ul>
  9. 9. <ul><li>Where is traffic concentrated between 6 a.m. and 8 a.m.? </li></ul>
  10. 10. <ul><li>Where is traffic concentrated between 6 p.m. and 8 p.m.? </li></ul>
  11. 11. <ul><li>Select only trips that start in the city centre (orange) and move to North-West </li></ul><ul><ul><li>Behaviours are still rather heterogeneous </li></ul></ul><ul><ul><li>Notice the O/D matrix navigation tool on the right </li></ul></ul>
  12. 12. <ul><li>Trajectory clustering divides trips based on the route they cover </li></ul><ul><ul><li>Different color = different group </li></ul></ul><ul><ul><li>Outliers are removed </li></ul></ul>
  13. 13. <ul><li>Three sample clusters are highlighted </li></ul><ul><ul><li>One group (red) goes straight to NW, the others follow alternative routes </li></ul></ul>
  14. 14. <ul><li>Temporal analysis on each group tells us when they perform the trip </li></ul><ul><ul><li>A small group in the morning (commuters working outside the city?) a much larger one in the afternoon (incoming commuters?). </li></ul></ul>
  15. 15. <ul><li>Origin/Destination analysis is flexible </li></ul><ul><ul><li>Analyze traffic from/to city areas to/from parking lots </li></ul></ul>
  16. 16. <ul><li>Focus on a specific (high frequency) parking lot, close to Linate airport </li></ul>
  17. 17. <ul><li>Analyze typical itineraries followed to reach such parking lot </li></ul><ul><ul><li>T-Patterns -> overall view </li></ul></ul>
  18. 18. <ul><li>T-Patterns: highlight one pattern that comes from the centre </li></ul>
  19. 19. <ul><li>T-Patterns: highlight one pattern that comes from North, along the “tangenziale” (ring road) </li></ul>
  20. 20. <ul><li>T-Patterns: highlight one pattern that comes from South, along the “tangenziale” (ring road) </li></ul>
  21. 21. <ul><li>Where is people between 6pm and 8pm of Wednesday, April 4 th ? </li></ul>
  22. 22. <ul><li>Where is people between 8pm and 10pm of Wednesday, April 4 th ? </li></ul><ul><ul><li>An high density spot appeared </li></ul></ul>
  23. 23. <ul><li>Where is people between 10pm and midnight of Wednesday, April 4 th ? </li></ul><ul><ul><li>The dense spot disappeared. What happened? </li></ul></ul>
  24. 24. <ul><li>Focus on the high-density spot </li></ul><ul><ul><li>Centered on the parking lots of the stadium </li></ul></ul><ul><ul><li>April 4 th , 2007: a football match took place there... </li></ul></ul>
  25. 25. <ul><li>Have a close look at when people arrived to the stadium, and when they left </li></ul><ul><ul><li>Through O/D matrix tool, focus on traffic from/to stadium area </li></ul></ul>
  26. 26. <ul><li>Arrivals and departures distributed as expected (concentrated resp. before and after the match) </li></ul><ul><li>Small surprising result: some people start leaving around 30 minutes before the match ended... </li></ul>

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