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Michael Szell mszell@mit.edu
Paolo Santi
Giovanni Resta
Stanislav Sobolevsky
Carlo Ratti
Steven Strogatz (Cornell)
Benedikt Groß
Joey Lee
Eric Baczuk
Carlo Ratti
Andi Weiß (47Nord)
Stefan Landsbeck (47Nord)
Research Visualization & Explorer
hubcab
Taxi-sharing in New York City: A network-based approach
Large GPS data sets on taxi movements
NYC
Singapore
13,500 cabs
26,000 cabs
Shanghai, San Francisco, Vienna, ...
Step 1: Analyze data
NY 170,000,000 trips / year
Pickups Dropoffs
Urban taxi systems
Pickups Dropoffs 7 days in 20 sec
Trips could be combined
Previous attempts at improvement
• Ride sharing
• Smart hailing
Can we come up with a new system?
• More efficient
• Less emissions
• Affordable alternative
Step 2: A new dispatch algorithm
Combine 2 trips
Step 2: A new dispatch algorithm
Combine k trips “Taxi Limousine”
Manhattan street network
4000 intersections
9000 street segments
Extracted from
OpenStreetMap
Match GPS-coords of
pickup/dropoff points with
street intersections
Dynamic pickup and delivery problems
T1
T2
T3
T4
Like traveling salesman
with time constraints
Small systems solvable
with linear programming
Large systems not
Yang, Jaillet and Mahmassani, Transp Sci 38 (2004)
Berbeglia, Cordeau and Laporte, Eur J Op Res 202 (2010)
Marin, An Op Res 143 (2006)
Shareability networks
k = 2
T1
T2
T3
T4
T2T1
T3
T4
Shareability networks
k = 2
T1
T2
T3
T4
T2T1
T3
T4
Solution: maximum matching
Generalizable to k>2
but unfeasible for k>3
Chandra and Halldorsson, J Alg 39 (2001)
Satisfaction criterion
Maximum time delay Δ
Δ = 30 sec Δ = 60 sec
more tolerance = denser network = more sharing opportunities
Krings et al, EPJ Data Sci 1 (2012)
Oracle vs. Online
Oracle: omniscient,
best possible
T1
T2
Online: realistic,
constrained by
time window δ
δ
Set δ = 1min
Step 2: A new dispatch algorithm
• Send destination request (via app)
• Wait δ min
• Receive sharing options
• Trip may be prolonged up to Δ min
How it works:
Consequences:
• Less traffic = less pollution etc
• Split costs for customers
Step 3: Simulation results: MOST trips can be combined!
Only δ = 1 min initial waiting time needed!
Online tool for interactive exploration
http://hubcab.org
(in development)
Zoom into the data
Pickups Dropoffs
Michael Szell mszell@mit.edu
Benedikt Groß
Joey Lee
Eric Baczuk
Carlo Ratti
Andi Weiß (47Nord)
Stefan Landsbeck (47Nord)
Research Visualization & Explorer
hubcab
Taxi-sharing in New York City: A network-based approach
Paolo Santi
Giovanni Resta
Stanislav Sobolevsky
Carlo Ratti
Steven Strogatz (Cornell)

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HubCab

  • 1. Michael Szell mszell@mit.edu Paolo Santi Giovanni Resta Stanislav Sobolevsky Carlo Ratti Steven Strogatz (Cornell) Benedikt Groß Joey Lee Eric Baczuk Carlo Ratti Andi Weiß (47Nord) Stefan Landsbeck (47Nord) Research Visualization & Explorer hubcab Taxi-sharing in New York City: A network-based approach
  • 2. Large GPS data sets on taxi movements NYC Singapore 13,500 cabs 26,000 cabs Shanghai, San Francisco, Vienna, ...
  • 3. Step 1: Analyze data NY 170,000,000 trips / year Pickups Dropoffs
  • 4. Urban taxi systems Pickups Dropoffs 7 days in 20 sec
  • 5. Trips could be combined
  • 6. Previous attempts at improvement • Ride sharing • Smart hailing
  • 7. Can we come up with a new system? • More efficient • Less emissions • Affordable alternative
  • 8. Step 2: A new dispatch algorithm Combine 2 trips
  • 9. Step 2: A new dispatch algorithm Combine k trips “Taxi Limousine”
  • 10. Manhattan street network 4000 intersections 9000 street segments Extracted from OpenStreetMap Match GPS-coords of pickup/dropoff points with street intersections
  • 11. Dynamic pickup and delivery problems T1 T2 T3 T4 Like traveling salesman with time constraints Small systems solvable with linear programming Large systems not Yang, Jaillet and Mahmassani, Transp Sci 38 (2004) Berbeglia, Cordeau and Laporte, Eur J Op Res 202 (2010) Marin, An Op Res 143 (2006)
  • 12. Shareability networks k = 2 T1 T2 T3 T4 T2T1 T3 T4
  • 13. Shareability networks k = 2 T1 T2 T3 T4 T2T1 T3 T4 Solution: maximum matching Generalizable to k>2 but unfeasible for k>3 Chandra and Halldorsson, J Alg 39 (2001)
  • 14. Satisfaction criterion Maximum time delay Δ Δ = 30 sec Δ = 60 sec more tolerance = denser network = more sharing opportunities Krings et al, EPJ Data Sci 1 (2012)
  • 15. Oracle vs. Online Oracle: omniscient, best possible T1 T2 Online: realistic, constrained by time window δ δ Set δ = 1min
  • 16. Step 2: A new dispatch algorithm • Send destination request (via app) • Wait δ min • Receive sharing options • Trip may be prolonged up to Δ min How it works: Consequences: • Less traffic = less pollution etc • Split costs for customers
  • 17. Step 3: Simulation results: MOST trips can be combined! Only δ = 1 min initial waiting time needed!
  • 18. Online tool for interactive exploration http://hubcab.org (in development)
  • 19. Zoom into the data Pickups Dropoffs
  • 20. Michael Szell mszell@mit.edu Benedikt Groß Joey Lee Eric Baczuk Carlo Ratti Andi Weiß (47Nord) Stefan Landsbeck (47Nord) Research Visualization & Explorer hubcab Taxi-sharing in New York City: A network-based approach Paolo Santi Giovanni Resta Stanislav Sobolevsky Carlo Ratti Steven Strogatz (Cornell)