Data collected from mobile phones has the potential to provide insight both into the relational dynamics of individuals (Eagle et al., Proceedings of the National Academy of Sciences, 106:15274-15278, 2009) and into human mobility patterns (Gonzalez et al., Nature, 453: 779-78, 2008). The high degree of temporal and spatial regularity showed in previous work prompts us to explore the potential of social networks as an infrastructure for transportation networks of small-portable physical goods. We propose an exchange platform where people lend each other objects of small value - such as ski boots or a star-shaped screwdrivers, which are transported by the owners or by carriers (friends or acquaintances of the owner in the social network). Participants in this exchange platform are not required to change their daily routines in order to deliver the items. By running simulations on mobile Call Detail Records - which include location information - from a large metropolitan area, we evaluate the performance of several transportation strategies such as direct transfer, nearest neighbor/only friends, nearest neighbor/everyone, and shortening the distance to the destination, in terms of the percentage of successfully transported objects, time to dispatch, number of hops and traveled distance. For the sake of the simulation we fixed that the delivery of an object was failed if it was still circulating in the network after a month since the injection in the simulation. Results show that completely unoptimized routing heuristics could successfully deliver an average of 3,908 objects --over 10,000 injected objects-- with an average delivery time of 0.59 days. These preliminary results suggest that, under considerably general assumptions, social networks may indeed be an effective and inexpensive infrastructure for transportation networks. This may have two important implications for sustainability. First, a service designed according to this principle might support people in reusing objects and tools that lie unused in one's premises (it was calculated that the average power tool is used 15 minutes in its entire life (Takara, 'In The Bubble', MIT Press, 2005)), therefore reducing the environmental pollution due to the production and transportation of new items. Second, leveraging people's routines to transport items might avoid additional environmental impact due to the transport of the shared items as if they were going to be delivered using a standard service.

1. Exploring Social Networks as
an Infrastructure for
Transportation Networks
Mauro Cherubini, Mengxiao Zhu (Northwestern),
Manuel Cebrian (MIT), and Nuria Oliver

2. SwapRelay
Peer-to-peer gets physical
intro → SwapRelay → scientific relevance → methodology → results → conclusion

3. sustainability
• minimize the waste of matter and energy
• use more people and less matter
• follow the whole-system approach

4. alternative economies

5. use, not own
The average consumer power tool is used for
10 minutes in its entire life –but it takes hundreds of
times its own weight to manufacture such an object.
[Takara, 2005]
intro → SwapRelay → scientific relevance → methodology → results → conclusion

6. SwapRelay
is a platform where people lend each other objects or
services and earn “swap points” that they can reuse to
borrow other items.
intro → SwapRelay → scientific relevance → methodology → results → conclusion

7. some details
• items are insured by credit-card
• objects are transported by owners or by
carriers
• carriers earn swap points
• swap points might be converted in real
money for specific services
intro → SwapRelay → scientific relevance → methodology → results → conclusion

8. challenges
• we do not want to change people’s daily
routines
• we would like to reuse existing
infrastructure
direct transfer
indirect
intro → SwapRelay → scientific relevance → methodology → results → conclusion

9. scientific contribution
• Exploring laws governing human
motion particularly focusing on
transfer of physical items
• Defining a stochastic optimization strategy
to exploit regularities in human mobility
Home Work Home Work Home Work
10pm 8am 10p 8am 10pm 8am
10pm
intro → SwapRelay → scientific relevance → methodology → results → conclusion

10. system questions
• would the system be sustainable (can it be
compared with snail mail)?
• what is the best incentive mechanism that
can persuade people to inject a minimum
number of items?
intro → SwapRelay → scientific relevance → methodology → results → conclusion

11. research questions
• How many days for an item to travel from
point A to point B?
• What’s the average distance an item has to
travel?
• What is the average number of hops that
the item has to go through?
• Trusted nodes vs. untrusted nodes
intro → SwapRelay → scientific relevance → methodology → results → conclusion

12. analysis of CDR data
We analyzed anonymous CDR data of a large
metropolitan area in a country with an
emerging economy.
Using this information we reconstructed the
social network and the mobility
patterns of customers.
Then, we simulated the peer-to-peer
exchange of items as if the service
existed and measured transfer performance.
intro → SwapRelay → scientific relevance → methodology → results → conclusion

13. data cleansing
dataset: #users 300K, #links 350K,
observation period 6 months
• took only most active users in the dataset:
at least 5 phone calls in a week to ensure
enough datapoints
• considered only mobile customers for
which we could detect a social network
• removed business and service numbers
• removed short calls, dropped calls, spam
intro → SwapRelay → scientific relevance → methodology → results → conclusion

14. constructing Social
Networks
• Exchange of phone calls (Martinez et al.,
2009)
• Number of calls between two users
exceed a certain number, for example, 5
interactions (JP Onnela et al., 2007)
• Direct calling neighbor and Indirect
calling neighbor (Kianmehr & Alhajj, 2009)
intro → SwapRelay → scientific relevance → methodology → results → conclusion

15. constructing Social
Networks
• Exchange of phone calls (Martinez et al.,
2009)
• Number of calls between two users
exceed a certain number, for example, 5
interactions (JP Onnela et al., 2007)
• Direct calling neighbor and Indirect
calling neighbor (Kianmehr & Alhajj, 2009)
Call duration and/or call frequency
intro → SwapRelay → scientific relevance → methodology → results → conclusion

16. criterium for SN
dataset: #components 350
intro → SwapRelay → scientific relevance → methodology → results → conclusion

18. inferring mobile patterns
Calculated
central point
Displacement
Radius of gyration Diameter of users
(Gonzalez et al., 2008) (Martinez et al., 2009)
intro → SwapRelay → scientific relevance → methodology → results → conclusion

19. criteria for location
• we divided the day into time slots of 1 hour and
we modeled each slot individually
• for the time slots for which we do not have data
we assume the person has not moved.
• probability of being in a location decreased by half
with time (to fill gaps)
• peers were in the same location if calling using the
same BTS
Antenna
B
A
C
intro → SwapRelay → scientific relevance → methodology → results → conclusion

21. details of the simulation
• 10K transactions over the course of 1 year
• for each injected item, we picked at
random the “lender” and the “borrower”
from the same social network
• we used 3 different heuristics for the
delivery with 2 variations: with friends and
with everybody
intro → SwapRelay → scientific relevance → methodology → results → conclusion

22. constraints
• we considered an item not delivered if it
was circulating in the simulation after 1
month from the injection in the simulation
• peers could do the exchange if they were
within 1 Km away from each other
(parameter)
• maximum 10 hops (parameter)
intro → SwapRelay → scientific relevance → methodology → results → conclusion

23. h1: direct transfer (dt)
The item can only be transferred between
the ‘lender’ and the ‘borrower’
direct transfer
intro → SwapRelay → scientific relevance → methodology → results → conclusion

24. h2: nearest neighbor (nn)
The current carrier of the item first checks if the target is
in the range. If yes, the item will be handed over to the
target, otherwise, the item will be given to the nearest
neighbor of user depending on whether or not non-friend
transfer is allowed.
intro → SwapRelay → scientific relevance → methodology → results → conclusion

25. h3: hot potato (hp)
The transfer rules are similar to nn. The only difference is
that here a carrier list is maintained to make sure that the
item is not given back to any one of the previous carriers.
intro → SwapRelay → scientific relevance → methodology → results → conclusion

26. NetLogo video / demo
intro → SwapRelay → scientific relevance → methodology → results → conclusion

27. performance
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intro → SwapRelay → scientific relevance → methodology → results → conclusion

28. performance
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intro → SwapRelay → scientific relevance → methodology → results → conclusion

29. travel distance
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intro → SwapRelay → scientific relevance → methodology → results → conclusion

30. travel distance
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intro → SwapRelay → scientific relevance → methodology → results → conclusion

31. hops and days
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intro → SwapRelay → scientific relevance → methodology → results → conclusion

32. hops and days
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intro → SwapRelay → scientific relevance → methodology → results → conclusion

33. conclusion
social networks may indeed be an effective
and inexpensive infrastructure
for transportation networks
intro → SwapRelay → scientific relevance → methodology → results → conclusion

34. implications
1) a service designed according to this principle might
support people in reusing objects and tools that lie unused
in one's premises (it was calculated that the average power
tool is used 10 minutes in its entire life [Takara, 2005])
2) leveraging people's routines to transport items might
avoid additional environmental impact due to the transport
of the shared items as if they were going to be delivered
using a standard service
intro → SwapRelay → scientific relevance → methodology → results → conclusion

35. future work
• extend simulation to different datasets
coming from different regions of the world
• test heuristics with more strict
assumptions on how SN are formed and on
the spatial proximity
• develop an optimization algorithm using the
history of phone calls
intro → SwapRelay → scientific relevance → methodology → results → conclusion

36. take away message:
social networks may indeed be an effective
and inexpensive infrastructure
for transporting physical goods
Q&A
Mauro Cherubini
mauro@tid.es
http://research.tid.es