Quantifying the potential of ride-sharing using Call
Description Records (CDRs)
Blerim Cici*, Athina Markopoulou*, Enrique Frías-Martínez**, Nikolaos Laoutaris**
*University of California, Irvine
**Telefonica Research
Outline
• Introduction • Mobility Data • Algorithms and Results
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Outline
• Introduction • Mobility Data • Algorithms and Results
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What is Ride-Sharing ?
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Benefits
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Ride-Sharing: An old idea
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Ride-Sharing in the past 1. Difficult to set up
2. Few opportunities
3. Inflexible
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Ride-Sharing now
1. Difficult to set up
2. Few opportunities 3. Inflexible
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Ride-Sharing now
1. Difficult to set up
2. Few opportunities 3. Inflexible
But, why it’s not mainstream yet ?
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Introduction • We want to investigate if ride-
sharing possible.
• Considered quantifiable parameters: 1. Distance tolerance 2. Distribution of departure times 3. Time tolerance
• An upper bound due to simplifications
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Parameters - Space
d : distance tolerance
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Parameters - Time
Time 8 am 5 pm
• σ : standard deviation of Home/Work departure times
• τ : time tolerance
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Contributions
• We used real location information to validate its potential
• We formulated ride-sharing as a facility location problem.
• We developed scalable and efficient algorithms to match the users.
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Outline
• Introduction • Mobility Data • Algorithms and Results
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Data
• Call Description Records (CDRs): – every phone call a new entry – the location of closest tower is recorded
• Our CDR dataset: – September – December 2009 – 5M users in Madrid
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Determining Home/Work
• Use existing methodology: – S. Isaacman, R. Becker, R. Caceres, S.
Kobourov, M. Martonosi, J. Rowland, and A. Varshavsky, “Identifying Important Places in People’s Lives from Cellular Network Data”, Pervasive 2011
• Home/Work locations of 272K users
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Outline
• Introduction • Mobility Data • Algorithms and Results
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Formulation
• Create Ride-Sharing groups: – All cars have capacity of 4 – Matching users who live and work close
by – Goal: is to minimize the number of cars
used
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Formulation • Capacitated Facility location with
Unsplittable Demands: – Facilities : Drivers – Clients : Passengers
• We choose as driver the user, who will minimize the distance traveled by his passengers.
• Large cost for every new driver
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EndPoints RS • NP hard !
• Inspired by: – M. Korupolu, C. Plaxton, and R. Rajaraman. “Analysis of a local search heurisFc for facility”, ACM-‐SIAM 1998
• EndPoints RS: – Efficient heuristic – Scalable
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EndPoints RS
• EndPoints RS: – Start with an initial “smart” solution – Iterative improvements by local search
in solution space
• Scalability – Fixed local search steps – Fix numbers of iterations
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Results for EndPoint RS
0.2 0.4 0.6 0.8 10
20
40
60
80
d (km)
% o
f car
s re
mov
ed
Success of end−point ride−sharing
Absolute upper boundTighter upper boundTime indifferento = 10, m = 10o = 10, m = 20
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EnRoute RS • Find Home/Work path
through Google Maps
• EnRoute RS: – Iterative algorithm – Fill empty seats by
pick-ups
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Results for EnRoute RS
0.2 0.4 0.6 0.8 10
20
40
60
80
km
% o
f car
s re
mov
ed
Success of en−route ride−sharing
Absolute upper boundTime indifferento = 10, m = 10o = 10 , m = 20
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Summary
• We evaluated the potential or ride-sharing in the city of Madrid.
• We used mobility data from CDRs of a major European Telco.
• There seems to be great potential for ride-sharing
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Limitations
• We assumed that people are willing to share a ride with strangers
• This is a strong assumptions. Our work shows only an upper bound
• In future work we plan to use social filtering
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Thank You
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