Where to Find My Next Passenger?

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1 Where to Find My Next Passenger? Jing Yuan 1 Yu Zheng 2 Liuhang Zhang 1 Guangzhong Sun 1 1 University of Science and Technology of China 2 Microsoft Research Asia September 19, 2011 Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

2 Motivation Taxis in big cities (103,000 in Mexico, 67,000+ in Beijing) Problems brought by cruising taxis: gas, time, profit Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

3 Motivation Taxis in big cities (103,000 in Mexico, 67,000+ in Beijing) Problems brought by cruising taxis: gas, time, profit, traffic jams, energy, air pollution Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

4 Motivation Taxis in big cities (103,000 in Mexico, 67,000+ in Beijing) Problems brought by cruising taxis: gas, time, profit, traffic jams, energy, air pollution Passengers are still hard to find a vacant taxi sometimes Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

5 Recommender Scenario P P P A) Taxi recommender Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

6 Recommender Scenario P P P A) Taxi recommender B) Passenger recommender Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

7 Recommender Scenario P P P A) Taxi recommender B) Passenger recommender Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

8 Recommender Scenario P P P A) Taxi recommender B) Passenger recommender Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

9 Data I Beijing Taxi Trajectories I 33,000 taixs in 3 month I Total distance: 400M km I Total number of points: 790M I Average sampling interval: 3.1 minutes, 600 meters I Beijing Road Network I 106,579 road nodes I 141,380 road segments Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

10 Profit-variant taxi drivers proportion Low High fare distance/unit time (m/s) occupied ratio high low overall time of day (hour) (a) Distribution of profit (b) Occupied ratio during a day Figure 1: Statistics on the profit distribution and occupied ratio Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

11 Cruise More, Earn More? cruising distance/unit time (m/s) r = fare distance/unit time (m/s) density Figure 2: Density scatter of cruising distance/unit time w.r.t. profit Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

12 System overview Online Recommendation Taxi Recommender Passenger Recommender Probabilistic Modeling Query Time (T0) and Location (LC) of a user Knowledge of parking places Knowledge of road segments Statistic learning Map-Matching Parking places Trips Parking Detection Segmentation Trajectories Offline Mining Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

13 System overview Online Recommendation Taxi Recommender Passenger Recommender Probabilistic Modeling Query Time (T0) and Location (LC) of a user Knowledge of parking places Knowledge of road segments Statistic learning Map-Matching Parking places Trips Parking Detection Segmentation Trajectories Offline Mining Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

14 Parking Place Detection Parking Place: the places where the taxis frequently wait for passengers. (not a parking slot). Candidates Generation Filtering Density-Based Clustering Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

15 Parking Place Detection Candidates Generation A group of points satisfying δ, τ; connect them if overlap exists p 2 p 4 p 1 p 6 p 2 p p p p 4 p 6 p 3 p 5 (A) δ p 3 p 5 (B) p 1 p 2 p 4 p p 5 3 p 6 p 7 (C) p 4 p 7 p 1 p 2 p 3 p 5 p 6 (D) Filtering Density-Based Clustering Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

16 Parking Place Detection Candidates Generation Filtering Distinguished from traffic jams (bagging classifier) features used: spatial-temporal(d c,mbr... ), POI,... MBRc A GPS point MBRr A road A POI A road MBRr dc MBRc A) Real parking place B) Traffic jam C) Features Density-Based Clustering Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

17 Parking Place Detection Candidates Generation Filtering Density-Based Clustering Aggregate the candidates belonging to a single parking place Railway Station Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

18 Parking Place Detection Railway Station Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

19 Taxi Recommender A good parking place (to go towards): the probability to pick up a passenger (Possibility) the expected duration from T 0 to the time the next passenger is picked up (Cost) the distance/duration of the next trip (Benefit) Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

20 Taxi Recommender A good parking place (to go towards): the probability to pick up a passenger (Possibility) the expected duration from T 0 to the time the next passenger is picked up (Cost) the distance/duration of the next trip (Benefit) Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

21 Taxi Recommender A good parking place (to go towards): the probability to pick up a passenger (Possibility) the expected duration from T 0 to the time the next passenger is picked up (Cost) the distance/duration of the next trip (Benefit) Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

22 Probability r 1 P 3 P 1 P 2 Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

23 Probability p 1 r 1 P 3 P 1 P 2 Situation 1: Pick up during the route at r 1 r i road segment i t i travel time from r 1 to r i p i the probability that a taxi picks up a passenger at r i (at time T 0 + t i ) Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

24 Probability p2 (1-p 1 ) r 2 r 1 P 3 P 1 P 2 Situation 1: Pick up during the route at r 2 r i road segment i t i travel time from r 1 to r i p i the probability that a taxi picks up a passenger at r i (at time T 0 + t i ) Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

25 Probability r 2 r 1 P 3 p 3 (1-p 1 )(1-p 2 ) r 3 P 1 P 2 Situation 1: Pick up during the route at r 3 r i road segment i t i travel time from r 1 to r i p i the probability that a taxi picks up a passenger at r i (at time T 0 + t i ) Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

26 Probability P 3 P 1 P2 Pr(W)p * Situation 2: Pick up at a parking place W the event that a taxi waits at a parking place t i travel time from r 1 to r i p the probability that a taxi picks up a passenger at a parking place (at time T 0 + t n ) Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

27 Probability Pr(W)(1-p * ) P 3 P 1 P 2 Situation 3: Fail to pick up a passenger W the event that a taxi waits at a parking place t i travel time from r 1 to r i p the probability that a taxi picks up a passenger at a parking place (at time T 0 + t n ) Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

28 Cost and Benefit Analysis Duration before the next trip T E[T S] =E[T R S] + E[T P S] n t i Pr(S i ) + t n Pr(S n+1 ) + Pr(W) m p j tj i=1 j=1 =. (1) Pr(S) Distance of the next trip D N Duration of the next trip T N Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

29 Recommendation Strategies 1 Taxi Recommender S1. Topk max {E[D N S]/E[T + T N S] : Pr(S) > P θ }. most profitable, given a probability guarantee. S2. Topk min {E[T S] : Pr(S) > P θ, D N > D θ }. fastest to find a passenger, given probability and distance guarantee S3. Topk max {Pr(S) : E[D N S]/E[T + T N S] > F θ }. most likely to find a passenger, given profit guarantee S Passenger Recommender r = argmax Pr(C; r t). r Ω Ω: search space within a walking distance Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

30 Recommendation Strategies 1 Taxi Recommender S1. Topk max {E[D N S]/E[T + T N S] : Pr(S) > P θ }. most profitable, given a probability guarantee. S2. Topk min {E[T S] : Pr(S) > P θ, D N > D θ }. fastest to find a passenger, given probability and distance guarantee S3. Topk max {Pr(S) : E[D N S]/E[T + T N S] > F θ }. most likely to find a passenger, given profit guarantee S Passenger Recommender r = argmax Pr(C; r t). r Ω Ω: search space within a walking distance Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

31 Recommendation Strategies 1 Taxi Recommender S1. Topk max {E[D N S]/E[T + T N S] : Pr(S) > P θ }. most profitable, given a probability guarantee. S2. Topk min {E[T S] : Pr(S) > P θ, D N > D θ }. fastest to find a passenger, given probability and distance guarantee S3. Topk max {Pr(S) : E[D N S]/E[T + T N S] > F θ }. most likely to find a passenger, given profit guarantee S Passenger Recommender r = argmax Pr(C; r t). r Ω Ω: search space within a walking distance Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

32 Recommendation Strategies 1 Taxi Recommender S1. Topk max {E[D N S]/E[T + T N S] : Pr(S) > P θ }. most profitable, given a probability guarantee. S2. Topk min {E[T S] : Pr(S) > P θ, D N > D θ }. fastest to find a passenger, given probability and distance guarantee S3. Topk max {Pr(S) : E[D N S]/E[T + T N S] > F θ }. most likely to find a passenger, given profit guarantee S Passenger Recommender r = argmax Pr(C; r t). r Ω Ω: search space within a walking distance Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

33 Recommendation Strategies 1 Taxi Recommender S1. Topk max {E[D N S]/E[T + T N S] : Pr(S) > P θ }. most profitable, given a probability guarantee. S2. Topk min {E[T S] : Pr(S) > P θ, D N > D θ }. fastest to find a passenger, given probability and distance guarantee S3. Topk max {Pr(S) : E[D N S]/E[T + T N S] > F θ }. most likely to find a passenger, given profit guarantee S Passenger Recommender r = argmax Pr(C; r t). r Ω Ω: search space within a walking distance Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

34 Recommendation Strategies 1 Taxi Recommender S1. Topk max {E[D N S]/E[T + T N S] : Pr(S) > P θ }. most profitable, given a probability guarantee. S2. Topk min {E[T S] : Pr(S) > P θ, D N > D θ }. fastest to find a passenger, given probability and distance guarantee S3. Topk max {Pr(S) : E[D N S]/E[T + T N S] > F θ }. most likely to find a passenger, given profit guarantee S Passenger Recommender r = argmax Pr(C; r t). r Ω Ω: search space within a walking distance Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

35 Evaluation on Parking Place Detection Key issue: traffic jams vs. parking places Features Precision Recall Spatial Spatial+POI Spatial+POI+Collaborative Spatial+POI+Collaborative+Temporal Table 1: Results of parking place filtering Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

36 Evaluation on Knowledge Learning proportion proportion proportion proportion proportion time of day (hour) waiting time (minute) time of day (hour) duration (minute) time of day (hour) distance (km) (a) waiting time (b) duration of the first trip (c) distance of the first trip Figure 3: Distribution in parking places (overall) probability road level time of day (hour) time of day (hour) duration (minute) time of day (hour) distance (km) (a) Pr(C O) (b) duration of the first trip (c) distance of the first trip Figure 4: Statistics results of road segments (overall) Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

37 Evaluation on Online Recommendation Precision (#hits/#recommendations) and Recall (#parking places the drivers actually go to/#suggested parking places) RME for the hit parking places on T, T N and D N. ndcg Ave(S1,S2,S3) B1 B k S1 S2 S3 B1 B2 Precision Recall RME(T) 0.15 RME(D N ) 0.02 RME(T N ) 0.03 Table 2: RME, precision and recall Figure 5: ndcg Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

38 Screenshot of Passenger Recommender Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

39 Screenshot of Taxi Recommender Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

40 Windows Phone 7 APP Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

41 Next Step Waiting time modeling for passenger recommender Queueing models for parking places More in-the-field study Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

42 Thanks! Jing Yuan Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, / 19

T-Finder: A Recommender System for Finding Passengers and Vacant Taxis

1 T-Finder: A Recommender System for Finding Passengers and Vacant Taxis Nicholas Jing Yuan, Yu Zheng, Liuhang Zhang, Xing Xie Abstract This paper presents a recommender system for both taxi drivers and