matlas: a case study on milan mobility

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MAtlas: a case study on Milano, Italy

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Page 1: MAtlas: A case study on Milan mobility

MAtlas: a case study on Milano, Italy

Page 2: MAtlas: A case study on Milan mobility

Dataset info GPS traces

17K private cars

one week of ordinary mobility

200K trips (trajectories)

Milan, Italy

Data donated by OCTO Telematics Italia

Page 3: MAtlas: A case study on Milan mobility

Overall view of trips performed in a single day (Wednesday, April 4th, 2007)

Difficult to understand anything

Page 4: MAtlas: A case study on Milan mobility

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

Page 5: MAtlas: A case study on Milan mobility

Distribution of lengths of the trips

Neat power-law → several short trips, few very long ones

Page 6: MAtlas: A case study on Milan mobility

Distribution of trip duration

Another power-law, similar shape

Page 7: MAtlas: A case study on Milan mobility

How do length and speed of trips correlate?

Average length grows with avg. speed (right plot)

Yet, only slow trips reach considerable length (left)

Page 8: MAtlas: A case study on Milan mobility

Where is traffic concentrated between midnight and 2 a.m.? (red = most intense)

Page 9: MAtlas: A case study on Milan mobility

Where is traffic concentrated between 6 a.m. and 8 a.m.?

Page 10: MAtlas: A case study on Milan mobility

Where is traffic concentrated between 6 p.m. and 8 p.m.?

Page 11: MAtlas: A case study on Milan mobility

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

Page 12: MAtlas: A case study on Milan mobility

Trajectory clustering divides trips based on the route they cover

Different color = different group

Outliers are removed

Page 13: MAtlas: A case study on Milan mobility

Three sample clusters are highlightedOne group (red) goes straight to NW, the others follow

alternative routes

Page 14: MAtlas: A case study on Milan mobility

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?).

Page 15: MAtlas: A case study on Milan mobility

Origin/Destination analysis is flexible

Analyze traffic from/to city areas to/from parking lots

Page 16: MAtlas: A case study on Milan mobility

Focus on a specific (high frequency) parking lot, close to Linate airport

Page 17: MAtlas: A case study on Milan mobility

Analyze typical itineraries followed to reach such parking lot

T-Patterns → overall view

Page 18: MAtlas: A case study on Milan mobility

T-Patterns: highlight one pattern that comes from the centre

Page 19: MAtlas: A case study on Milan mobility

T-Patterns: highlight one pattern that comes from North, along the “tangenziale” (ring road)

Page 20: MAtlas: A case study on Milan mobility

T-Patterns: highlight one pattern that comes from South, along the “tangenziale” (ring road)

Page 21: MAtlas: A case study on Milan mobility

Where is people between 6pm and 8pm of Wednesday, April 4th?

Page 22: MAtlas: A case study on Milan mobility

Where is people between 8pm and 10pm of Wednesday, April 4th?

An high density spot appeared

Page 23: MAtlas: A case study on Milan mobility

Where is people between 10pm and midnight of Wednesday, April 4th?

The dense spot disappeared. What happened?

Page 24: MAtlas: A case study on Milan mobility

Focus on the high-density spot

Centered on the parking lots of the stadium

April 4th, 2007: a football match took place there...

Page 25: MAtlas: A case study on Milan mobility

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

Page 26: MAtlas: A case study on Milan mobility

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...