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MAtlas: A case study on Milan mobility
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MAtlas: A case study on Milan mobility

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