Make it difficults the creations of big communities working on the same projects
github
~31M developers
~96M repository
~20k contributors (largest repository)
wikipedia [english]:
~450K editors monthly
~5M edits monthly
~200k new pages monthly
OpenStreetMap:
~1M contributors
~2M points added per day
~200k new ways added per day
And Games? People play avery days often to a very complicated games
crosswords, sudoku etc.
chess, go, domino
SimCity, fortnite, Minecraft etc..
Citizen Science
SETI@home [1999]: analyze radio signals, searching for signs of extraterrestrial intelligence . People can partecipate using their PC, donating their computational resources.
foldit [2008]: fold the structures of selected proteins as perfectly as possible, using tools provided in the game. Nature paper with credits more than 57000 authors.
Quantum Moves [2012]: simulations of logical operations in a quantum computer. Played over 8 million times by more than 200,000 players worldwide.
The 200 000 players were all beaten by the stochastic optimization method. :(
Motivation 3
I was born in Rome
I had a very difficult childhood
Rome public transport are "not so good".
Ok. But how much compared to the other cities?
Where is the better served [by public transport] place in the city?
And in the world?
CityChrone: the context
Urban Accessibility measures
Huge scientific literature
The first definition of accessiblity in urban context is done more than 50 years ago
Many different definitions of accessibility
But no attemp to compute it at large scale.
A science of city needs quantitative measurement
This work must be considered, first of all, as an empirical work. It defines procedures to measure quantities and then we measured them.
Prerequisites
Data, algorithms and data visualization.
Quantity Definition
Easy to understand, easy to compute and meaningful quantities to measure public transport efficiency.
Collective creativity
Exploring the huge and complex space of new configurations of the public transport in cities
Data, visualizations, algorithms
Open data sources used
Public Transports Schedules (GTFS format) -
transitfeeds
This new class of algoritms are easy to implements and fast, but they have some crucial limitations in urban context.
We modified the CSA and the RAPTOR algorithm in order to use it in urban context.
CityChrone
Science for City
Boundaries and Tessellation.
It is possible to compute isochrones
First step towards an accessibility measure:
The larger isochrones are, the faster you move.
Velocity Score
Consider the Area of the Isochrone a time \(t\) computed in \(P\):
\begin{equation}
r(t,P) = \sqrt{\frac{A(t, P)}{\pi}}
\end{equation}
dividing by time, we obtain a quantity with the dimension of a velocity:
\begin{equation}
v(t,P) = \frac{r(t,P)}{t}
\end{equation}
Integrating over time:
\begin{equation}
v_{score}(P) = \int_0^{\infty} v(t, P) f(t) dt,
\end{equation}
\(f(t)^1\) is the daily time budget distribution for public transport.
The Velocity Score can be consider as the average velocity of a daily typical trip taking a random direction from \(P\).
\(^1\) Robert Kölbl, Dirk Helbing. Energy laws in human travel behaviour. New Journal of Physics 5, 48 IOP Publishing, 2003.
Consider the populations inside the Isochrone a time \(t\) computed in \(P\):
\begin{equation}
s(t,P) = \sum_{i \mid t_i(P) < t} p(h_i),
\end{equation}
we sum over all the hexagons with time \(t_i\) less than \(t\) and \(p(h_i)\) is the population within \(h_i\).
Exponential decay of the Velocity Score with the time distance from the center.
Exponential decay from the center of the city.
Exponential decay of the Sociality Score with the time distance from the center.
Why these patterns are observed in all cities?
Are these inequalities unavoidable?
Can be modified or optimized?
In which way?
I don't known.
CityChrone
Interactive platform
Now I know how much Rome public transports suck
What we have to do to reach Paris?
What are the best interventions given a budget?
Let's Play!
CityChrone
Interactive platform for exploring new scenario
Budget: 5 Bilion €
Name Scenario: Gram Author: Pietro
After 1 year
Name Scenario: rer + circle Author: mat
The future of public transports in cities
Bad ending for my current research, but happing ending for public transport in the cities?
Cars per 1000 inhabitants
Italy togheter with USA has the highest level of car ownership.
Italy
cars
Europe
cars
Rome
800
Paris
225
Milan
596
London
298
Turin
600
Barcellona
350
Catania
700
Berlin
297
Average person per car 1.2
95% of the time the cars are parked
Self driving cars (they are around us)
No property - No Parking
Boost in efficency
Sharing Trips
from taxy sharing to trip sharing\(^{1,2,3}\)
At least 50% less cars circulating
Public transport on demand
shrinking of the cost urban transportation of almost 10 times.
1. P. Santi, G. Resta, M. Szell, S. Sobolevsky, S. Strogatz, C. Ratti. Taxi pooling in New York City: a network-based approach to social sharing problems (2013).