T-Share: A Large-Scale Dynamic Taxi Ridesharing Service

1. T-Share: A Large-Scale Dynamic Taxi Ridesharing Service Shuo Ma, Yu Zheng, Ouri Wolfson Microsoft Research Asia University of Illinois at Chicago

2. Background • Taxi-sharing is of great social and environmental importance • Serving more demands: Peak hours vs Off-peak hours • Reduce energy consumption and air pollutants emission • Could save taxi fares while increasing the income of taxi drivers

5. Background • Challenges • Dynamic: • Dynamic queries: anytime and anywhere, lazy users • Dynamic taxis • Real-time query processing • large-scale: millions of users and tens of thousands of taxis • Wide range of applications • Private vehicles • Logistic industry for transporting goods

6. Value • Government • Save 800 million liter gasoline per year • Supporting 1M cars for 10 months • Worth about 1 billion USD • 1.64 billion KG CO2 emission • Passengers • Serving rate increased 300% • Save 42% expense on average • Taxi drivers increase profit 16% on average

7. Problem Definition • Query =<, , > • Origin and destination: and • Time window for pickup: , • Time window for delivery: , Given a fixed number of taxis traveling on a road network and a stream of queries, we aim to serve each query in the stream by dispatching the taxi which satisfies with the minimum increase in travel distance.

8. Architecture

9. Spatio-Temporal Index • Grid-based approximation • Select an anchor node in each grid

10. Spatio-Temporal Index • For each Grid • Spatially-ordered grid cell list (spatial closeness) • Temporally-ordered grid cell list (temporal closeness) • Taxi list sorted by the arrival time

11. Taxi Searching

12. Taxi Searching • Single-side taxi search • is located in • Merge taxi lists • Problem • Many candidate taxis • Scheduling process is heavy

13. Dual-Side Taxi Searching • Origin side • in • Destination side • in

15. Scheduling Module • Calculate schedule for each candidate taxi

16. Scheduling Module • Feasibility check • Two steps: first insert and then • Do not change the order of an existing schedule • Minimize the increase of travel distance • Given a schedule composed of points • positions to insert • positions to insert • possible ways of insertion

17. Scheduling Module • Feasibility check (using as an example) • : the time spent on waiting for the passenger • If , fail

18. Scheduling Module • Lazy Shortest Path Calculation • Find a lower bounder of travel time between two points • 1. ) )+ D - O • 3.

19. Pricing Scheme • Taxi fare per mile is higher for multiple passengers than for a single passenger • The taxi fare of shared distances is evenly split among the riding passengers

20. Evaluation • Settings • A trajectory dataset generated by over 33,000 taxis in Beijing over 3 months • Built experimental platform based on the data • Big data • 400 million kilometres • 790 million points • 20 million trips (46% occupied)

21. Evaluation • Experimental platform • Learn the distribution of queries on the road network over time of day from the data • Assume the arrival of queries follows a Poisson distribution • Learn the transition probability between different road segments • #. Of queries

22. Settings of experimental platform

23. Evaluation • Baselines • No ridesharing • Single-side and First Fit Ridesharing (SF) • Single-side and Best-fit Ridesharing (SB) • Dual-side and First Fit Ridesharing (DF) • Dual-side and Best-fit Ridesharing (DB)

24. Results • Effectiveness

25. Results • Efficiency

26. Conclusion • Win-win-win scenario • Candidate taxi selection based on a spatio-temporal index • Dual-side search saves 50% computational load • Have the similar effectiveness as compared with the single-side search • Taxi scheduling based on • Feasibility check • Lazy shortest path computing saves 83% computational load • Serve 720k queries per hour on a single machine • Future work • Consider more constraints: monetary constraints • Dynamic time estimation • Other factors: like social trust and credit

27. Thanks! Yu Zheng yuzheng@microsoft.com Homepage