Where to Find My Next Passenger? · IPassengers are still hard to ﬁnd a vacant taxi sometimes...

Where to Find My Next Passenger?

Jing Yuan1 Yu Zheng2 Liuhang Zhang1 Guangzhong Sun1

1University of Science and Technology of China2Microsoft Research Asia

September 19, 2011

Jing Yuan et al. (USTC,MSRA) Where to Find My Next Passenger? September 19, 2011 1 / 19

Motivation

I Taxis in big cities (103,000 in Mexico, 67,000+ in Beijing)I Problems brought by cruising taxis: gas, time, profit


Motivation

I Taxis in big cities (103,000 in Mexico, 67,000+ in Beijing)I Problems brought by cruising taxis: gas, time, profit, traffic jams,

energy, air pollution


Motivation

I Taxis in big cities (103,000 in Mexico, 67,000+ in Beijing)I Problems brought by cruising taxis: gas, time, profit, traffic jams,

energy, air pollutionI Passengers are still hard to find a vacant taxi sometimes


Recommender Scenario

P

P

P

A) Taxi recommender



P

P

P

A) Taxi recommender B) Passenger recommender



P

P

P




P

P

P



Data

I Beijing Taxi TrajectoriesI 33,000 taixs in 3 monthI Total distance: 400M kmI Total number of points:

790MI Average sampling interval:

3.1 minutes, 600 meters

I Beijing Road NetworkI 106,579 road nodesI 141,380 road segments


Profit-variant taxi drivers

2 4 60

0.05

0.1

0.15

0.2

fare distance/unit time (m/s)

prop

ortio

n

Low High

(a) Distribution of profit

1 4 7 10 13 16 19 220.4

0.5

0.6

0.7

0.8

time of day (hour)oc

cupi

ed r

atio

highlowoverall

(b) Occupied ratio during a day

Figure 1: Statistics on the profit distribution and occupied ratio


Cruise More, Earn More?

fare distance/unit time (m/s)

crui

sing

dis

tanc

e/un

it tim

e (m

/s)

density−→

r = 0.0874

0 1 2 3 4 5 6 70

1

2

3

4

Figure 2: Density scatter of cruising distance/unit time w.r.t. profit


System overview

Trajectories

Parking Detection

Parking places

Segmentation

Trips

Map-MatchingStatistic learning

Knowledge of road segments

Offline Mining

Knowledge of parking places

Taxi Recommender Passenger Recommender

Online Recommendation

Probabilistic Modeling

Time (T0) and Location (LC)

of a user

Query


System overview

Trajectories

Parking Detection

Parking places

Segmentation

Trips

Map-MatchingStatistic learning

Knowledge of road segments

Offline Mining

Knowledge of parking places

Taxi Recommender Passenger Recommender

Online Recommendation

Probabilistic Modeling

Time (T0) and Location (LC)

of a user

Query


Parking Place Detection

Parking Place: the places where the taxis frequently wait forpassengers. (not a parking slot).

I Candidates GenerationI FilteringI Density-Based Clustering



I Candidates GenerationA group of points satisfying δ, τ; connect them if overlap exists

p1

p7

p1p7

p1

p2

p3

p4

p5

p6 p7

p1p2

p3

p4

p5

p6p7

(A) (B)

(C) (D)

p3

p4

p5

p6

p3

p4

p5

p6

p2

δ

p2

I FilteringI Density-Based Clustering



I Candidates GenerationI Filtering

Distinguished from traffic jams (bagging classifier)features used: spatial-temporal(dc,MBR. . . ), POI, . . .

A) Real parking place B) Traffic jam

MBRc

MBRr

MBRc

MBRr

A road

A roadA GPS point A POI

dc

C) Features

I Density-Based Clustering




Aggregate the candidates belonging to a single parking place

Railway Station




Railway Station


Taxi Recommender

A “good” parking place (to go towards):I the probability to pick up a passenger ⇑ (Possibility)I the expected duration from T0 to the time the next passenger is

picked up ⇓ (Cost)I the distance/duration of the next trip ⇑ (Benefit)


Taxi Recommender




Taxi Recommender




Probability


r1

P2

P1

P3

ri road segment i

ti travel time from r1 to ri

pi the probability that a taxi picks up a passenger at ri (at time T0 + ti)

Probability


r1

p1

P2

P1

P3

Situation 1: Pick up during the route at r1

ri road segment i



Probability


r2r1

P2

P1

P3p2(1-p1)


ri road segment i



Probability


r2r1

P2

P1

P3

p3(1-p1)(1-p2) r3


ri road segment i



Probability


Pr(W)p*P2

P1

P3

Situation 2: Pick up at a parking place

W the event that a taxi waits at a parking place


p∗ the probability that a taxi picks up a passenger at a parking place (at time T0 + tn)

Probability


Pr(W)(1-p*)

P2

P1

P3

Situation 3: Fail to pick up a passenger

W the event that a taxi waits at a parking place


p∗ the probability that a taxi picks up a passenger at a parking place (at time T0 + tn)

Cost and Benefit Analysis

I Duration before the next trip T

E[T |S]

=E[TR|S] + E[TP|S]

=

n∑i=1

ti Pr(Si) + tn Pr(Sn+1) + Pr(W)m∑

j=1pj∗t∗j

Pr(S). (1)

I Distance of the next trip DN

I Duration of the next trip TN


Recommendation Strategies

1 Taxi RecommenderS1. TopkmaxE[DN |S]/E[T + TN |S] : Pr(S) > Pθ.

most profitable, given a probability guarantee.

S2. TopkminE[T |S] : Pr(S) > Pθ,DN > Dθ.fastest to find a passenger, given probability and distanceguarantee

S3. TopkmaxPr(S) : E[DN |S]/E[T + TN |S] > Fθ.most likely to find a passenger, given profit guarantee

S4. . . .2 Passenger Recommender

r = argmaxr∈Ω

Pr(C; r|t).

Ω: search space within a walking distance








r = argmaxr∈Ω

Pr(C; r|t).









r = argmaxr∈Ω

Pr(C; r|t).









r = argmaxr∈Ω

Pr(C; r|t).









r = argmaxr∈Ω

Pr(C; r|t).









r = argmaxr∈Ω

Pr(C; r|t).



Evaluation on Parking Place Detection

I Key issue: traffic jams vs. parking places

Features Precision RecallSpatial 0.695 0.670Spatial+POI 0.716 0.696Spatial+POI+Collaborative 0.725 0.706Spatial+POI+Collaborative+Temporal 0.909 0.889

Table 1: Results of parking place filtering


Evaluation on Knowledge Learning

15

913

1726

1014

1822

0

0.1

0.2

0.3

0.4

waiting time (m

inute)time of day (hour)

prop

ortio

n

(a) waiting time

10203040506026

1014

1822

0

0.2

0.4

0.6

duration (minute)

time of day (hour)

prop

ortio

n(b) duration of the first trip

5101520253026

1014

1822

0

0.2

0.4

0.6

distance (km)

time of day (hour)

prop

ortio

n

(c) distance of the first tripFigure 3: Distribution in parking places (overall)

0123456

26

1014

1822

0

0.05

0.1

0.15

time of day (hour)

road level

prob

abili

ty

(a) Pr(C O)

10203040506026

1014

1822

0

0.2

0.4

0.6

0.8

duration (minute)

time of day (hour)

prop

ortio

n

(b) duration of the first trip

5101520253026

1014

1822

0

0.2

0.4

0.6

distance (km)

time of day (hour)

prop

ortio

n

(c) distance of the first tripFigure 4: Statistics results of road segments (overall)


Evaluation on Online Recommendation

I Precision (#hits/#recommendations) and Recall (#parking placesthe drivers actually go to/#suggested parking places)

I NDCG@kI RME for the hit parking places on T, TN and DN .

1 2 3 4 50.2

0.3

0.4

0.5

0.6

k

nDC

G

Ave(S1,S2,S3)B1B2

Figure 5: nDCG

S1 S2 S3 B1 B2

Precision 0.63 0.66 0.67 0.60 0.61

Recall 0.59 0.65 0.64 0.57 0.52

RME(T) 0.15

RME(DN ) 0.02

RME(TN ) 0.03

Table 2: RME, precision and recall


Screenshot of Passenger Recommender


Screenshot of Taxi Recommender


Windows Phone 7 APP


Next Step

I Waiting time modeling for passengerrecommender

I Queueing models for parking placesI More in-the-field study


Thanks!

Jing [email protected]


[email protected]

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Where to Find My Next Passenger? · IPassengers are still hard to ﬁnd a vacant taxi sometimes...

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