MAtlas: A case study on Milan mobility

MAtlas : a case study on Milano, Italy

Dataset info
GPS traces
17K private cars
one week of ordinary mobility
200K trips (trajectories)
Milan, Italy
Data donated by
OCTO Telematics Italia

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

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

Distribution of lengths of the trips
Neat power-law -> several short trips, few very long ones

Distribution of trip duration
Another power-law, similar shape

How do length and speed of trips correlate?
Average length grows with avg. speed (right plot)
Yet, only slow trips reach considerable length (left)

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

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

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

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

Trajectory clustering divides trips based on the route they cover
Different color = different group
Outliers are removed

Three sample clusters are highlighted
One group (red) goes straight to NW, the others follow alternative routes

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?).

Origin/Destination analysis is flexible
Analyze traffic from/to city areas to/from parking lots

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

Analyze typical itineraries followed to reach such parking lot
T-Patterns -> overall view

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

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

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

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

Where is people between 8pm and 10pm of Wednesday, April 4 th ?
An high density spot appeared

Where is people between 10pm and midnight of Wednesday, April 4 th ?
The dense spot disappeared. What happened?

Focus on the high-density spot
Centered on the parking lots of the stadium
April 4 th , 2007: a football match took place there...

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

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...

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MAtlas: A case study on Milan mobility

by Knowledge Discovery and Data Mining on May 06, 2010

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MAtlas: A case study on Milan mobility — Presentation Transcript