1. Dynamic Ride-sharing Systems: a Case Study in Metro Atlanta Niels Agatz Joint work with Alan Erera, Martin Savelsbergh, Xing Wang ARC February 26, Atlanta

2. Motivation  Observations Congestion is a major issue in urban areas around the world Congestion leads to Loss of productivity Wasted fuel Pollution Private car occupancies are low Average of 1.8 for leisure trips Average of 1.1 for commuting trips

3. Motivation  Opportunity Effective use of empty car seats can Reduce congestion Reduce fuel consumption Reduce pollution Increase productivity Enabling technology exists GPS-enabled mobile devices Mobile-to-mobile communication Route guidance technology Dynamic Ride-Sharing

4. Outline  Introduction to dynamic ride-sharing  A dynamic ride-share setting  Solving the problem  Simulation Metro Atlanta  Simulation Results

5. 1.Announcement of trips 2.Matching of drivers and riders 3.Execution of identified trips 4.Sharing of trip costs Dynamic ride-sharing: basics Assumption: if no trip is identified, drivers and riders use their own car to reach destination

6. Dynamic ride-sharing: features  Dynamic: established on short-notice  Non-recurring trips: different from carpooling  Automated matching: different from online notice- boards  Independent drivers: different from taxis  Cost-sharing: variable trip related expenses  Prearranged: different from spontaneous, casual ride-sharing

7. Dynamic ride-sharing: providers  IPhone applications:  Carticipate (Aug. ‘08)  Avego (Dec. ‘08)  Google Android applications:  Piggyback (May ‘08)  Web-based:  Zebigo, PickupPal, Nuride, Rideshark, Ridecell, Goloco, Zimride, …, etc.

8. What is in it for participants?  Save costs by sharing fuel expenses and tolls  Save time through use of HOV-lanes  Save the planet

9. What is in it for providers?  Private: take a cut of the participant’s cost savings  Public (society): decrease external costs of transportation: emissions, pollution, and traffic congestion

10. Objectives  Participants: ↓ travel costs  Private ride-share provider: ↓ total travel costs  ↑ cut  Society: ↓ pollution and traffic congestion  Objectives are aligned: ↓ system-wide vehicle miles, win-win-win

11. Ride-share Setting Trip Announcement:  Trips between origin - destination  Role: driver, rider or flexible  Participant wants to save $  At most: a single driver - a single rider  No Transfers or multi-passenger trips

12. Announcement: Time Information Announcement time Departure time Latest arrival time direct travel timeflexibility time window for matching Lead-time Earliest Latest Relative to duration of trip?

13. Round Trips  Most trips in practice are round trips  Announcement round trips: jointly or separately?  Rider may want assurance return ride before departure  Return trip alternatives?

14. System Dynamics  New announcements continuously enter and leave the system!  People leave because ride-share was found  Announcement expired

15. Effectively Handling Dynamics  Run optimization at  specific time intervals;  After a given number of announcements.  Optimization Engine:  Input: trip announcements  Output: driver-rider matches  Commit tentative match as-late-as-possible!  shortly before latest departure of ride-share

16. Effectively Dealing with Dynamics

17. Run 1 Expired

18. Effectively Dealing with Dynamics Run 2 Ride-share finalized

19. Simulation Environment Based on traffic model data from ARC

20. Ride-share Characteristics Focus: Home-based Work, SOV  Ride-share participation rate (bc 2%)  Driver – Rider ratio (bc 1:1)  Announcement lead-time (abs) (bc 30 min)  Time flexibility (abs) (bc 20 min)

21. Announcement Generator  Randomly generate trip announcements based on o-d trip data: 2024 Traffic Analysis Zones (TAZs) # work-related round trips per day: 2.96 million ~ 87% single occupancy trips # O-D pairs: 2.90 million max # trips per O-D pair: 881 min # trips per O-D pair: 0.01

22. Generate time information: 1.Home-work  Draw latest departure: 7:30am, sd. 1h  Calculate: earl. departure, lat. arrival, announcement 2.Work-home  Draw time at work: 8 hour, sd. 30 min  Add time at work to announcement times Announcement Generator cont’d

23. Ride-share Scenarios  SEPAR: separate announcements for round trip, no guaranteed ride back  JOINT: joint announcement for round trip, guaranteed ride back (not necessarily with same driver!)  BOUND: all daily trips known upfront (no dynamics)

24. Simulation Results SUCCESS OUTWARD (riders) SUCCESS RETURN (riders) ∆ VHM∆ TRIPS SEPAR76.4%96.7%-28.7%-37.6% JOINT68.2%100%-21.3%-33.7% BOUND75.0%100%-24.0%-37.6% -8.2 ! -0.6% of total trips

25. Sensitivity  Increase/ decrease participation rate More + better matches!

26. Sensitivity cont’d  Flexible roles → success rate ↑  ↑ time flexibility → success rate ↑  response time ↓ → success rate ↓  ↑ lead-time → success rate ↑

27. Cost savings  Total daily system-wide savings: $53,000  20% cut Ride-share provider: $10,600  Viable business model?  Avg. savings pp matched: $1.4 (20%)  Enough incentive to participate? Input: $0.55 per mile (AAA 2007)

28. Environmental benefits  System-wide vehicle-miles: -105,519  Vehicle Trips: -15,400 Emissions  Hydrocarbons: -650 lbs/ day  CO: -4000 lbs / day  Oxides of Nitrogen: -323 lbs/ day

29. When can we find ride-shares? Density is good

30. What if we offer awards? Subsidize ride-share by a fixed award per match No award Award = $0.55 Award = $1.10 $9,500$19,000 +8%+2.5%

31. Under development  Study system performance over time  Key observations:  New participants try ride-sharing  Dissatisfied participants may stop announcing trips  Inspired by innovation diffusion models:  Inflow new users dependent on size of user pool!  Outflow depends on cumulative success rate

32. Outlook  More realistic travel time settings Time-dependent travel times  Behavioral modeling Match acceptance Cancelations No-shows Choices …

33. Thank You