mining regular routes from gps data for ridesharing recommendations
DESCRIPTION
Mining Regular Routes from GPS Data for Ridesharing Recommendations. Wen He , Deyi Li, Tianlei Zhang, Lifeng An, Mu Guo, and Guisheng Chen Tsinghua University Chinese Institute of Electronic System Engineering August 12, 2012. Regular Route - PowerPoint PPT PresentationTRANSCRIPT
Mining Regular Routes from GPS Data for Ridesharing Recommendations
Wen He, Deyi Li, Tianlei Zhang, Lifeng An, Mu Guo, and Guisheng Chen
Tsinghua University
Chinese Institute of Electronic System Engineering
August 12, 2012
Regular Route
A regular route is a complete route which often happen at a similar time
Commute route pick up children each day…
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OutlineIntroductionArchitectureDetails of solutionExperiments resultsConclusion
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BackgroundTraffic congestion has become a worldwide problem
Low vehicle occupancy
RideSharing becomes an attractive way to relieve traffic
pressure
4
0,5
1,6
2,7
3,8
4,9
Monday
Tuesday
Wednesday
Thursday
Friday
Challenges in RidesharingComplexity“Stranger danger”Reliability
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driver
rider
Our WorkMining Regular Routes from GPS Data for
Ridesharing Recommendations
Common method
Complexity
“Stranger danger”
Reliability
Our method
Automatic matching
Traveled regularly for a
period of time
More information from
GPS logs
6
Vs.
ChallengesUncertainty in time property
Start at different time
Complexity in traffic condition
Multiple transportation modePrivate driving
Public transportation
Uncertainty in route sequenceGPS signal drift
Obstacle in the road
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Database-basedUser- based
GPS Logs
Regular Routes MiningRoutes
Grouping
Regular Routes Finding
Routes Processing Stay Regions Subtracting
Grids Mapping
Routes SplittingTravel Modes
Reconizing
Ridesharing Recmmendations
Grid-based Routes Table
Building
Routes Matching
Recommendations
Architecture
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Routes Processing
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1514 1516 1518 1520 1522 1524 1526 1528644
645
646
647
648
649
650
651
652
653
654
655
116.294 116.296 116.298 116.3 116.302 116.304 116.306 116.30840.002
40.004
40.006
40.008
40.01
40.012
40.014
40.016
116.296 116.298 116.3 116.302 116.304 116.306 116.30840.004
40.005
40.006
40.007
40.008
40.009
40.01
40.011
40.012
40.013
40.014
116.296 116.298 116.3 116.302 116.304 116.306 116.30840.004
40.005
40.006
40.007
40.008
40.009
40.01
40.011
40.012
40.013
40.014
Stay Region
(a) A sample Trajectory Sequence (b) Stay Region Finding
Route 1
Route 2StartEnd
Dthreh
Ending Point
Begining Point
(c) Trajectory sequence after stay region subtracting
(d) the two routes after routes spilitting
A fragment of GPS Log
tc
ta tb
td
Route1 Route2 Route3
Tthresh
Stay region
Routes Grouping
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1 3 5 7 9 11 13 15 17 19 21 23
10.2310.2410.2710.2810.2910.3010.3111.311.411.511.611.711.1011.1111.1211.1311.1411.1711.1811.1911.2011.2111.2411.2511.26
time of day(hour)
Dat
e
t
One user’s routes during one month
(t- STthreh, t+ STthreh)t
Totalnumber
t-Routes (t-60min,t+60min)
1 18 3, 4, 8, 19, 22, 24, 28, 35, 37, 42, 44,47, 56, 58, 62, 64, 76, 78
2 2 4, 8
3 2 5, 8
4 5 5, 8, 17, 25, 73
5 8 1, 5, 6, 17, 25, 38, 45, 73
6 9 1, 6, 7, 25, 29, 38, 39, 45, 59
7 6 1, 7, 29, 39, 59, 60
8 2 26, 60
9 1 29
10 5 2, 20, 48, 74, 79
11 8 2, 18, 20, 23, 48, 57, 74, 79
12 10 2, 18, 23, 40, 43, 28, 57, 61, 63, 79
13 14 18, 21, 27, 36, 40, 41, 43, 46, 49, 57,61, 63, 75, 77
14 7 21, 27, 36, 41, 46, 49, 61, 65, 75, 77
15 6 41, 46, 49, 65, 75, 77
16 5 42 58 133 145 153、 、 、 、
18 0… … …
Frequent Directed Edges (FDE) Finding
DE.fre> fthreh FDE
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T1
T2T3
R1R2R3
(a) Raw Trajectories
(b) Routes after grids mapping
Directed Edge,DEm
DEm.fre=2DEm.sup={R1,R3}
One simple example --- FDEs finding
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0 1 2 3 4 5 6 7 8 9 10 11 120
1
2
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8
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12AB
C
D
E
F
G
H
I
J
K
L M N O P Q R S T U V W
AM->AN 2AM->BM 1AN->BN 3BM->CM 2BN->CN 3CM->DM 2CN->CO 1CN->DN 2CO->CP 3CP->DP 3DM->EM 2DN->DO 2…
Regular Routes Finding
130 1 2 3 4 5 6 7 8 9 10 11 120
1
2
3
4
5
6
7
8
9
10
11
12AB
C
D
E
F
G
H
I
J
K
L M N O P Q R S T U V W
R1 R2 R3 R4 R5Route:
If most of its DEs are FDEs
a candidate of an RR
fc(R) = m/nn ： number of DEs in Rm ： number of FDEs in R
FDE:
If most of its support routes
are candidate routes
part of an RR (RFDE)
Regular route:
a link of RFDEs
numDE
ithrehic fcDEfrcDE
.
1
))sup.((.
Mining Travel Modes of Regular RoutesFeature of Fixed Stop Rate (FSR)
Stop rate: number of points with low velocity [Zheng,Ubicomp 2008] ( accuracy: 0.6)Stop region: a user usually passed this region with a low velocityFixed stop rate: the number of stop regions in a route
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0 5 10 15 20 25 30 35 40 45 500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
No. FDEs in a RR
Sto
p P
roba
bili
ty
0 10 20 30 40 50 60 70 80 900
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
No. of FDEs in a RR
Sto
p P
roba
bili
ty
(a) SP of a RR generated by bus (b) SP of a RR generated by car
Vthre (km/h)
Acc
ura
cy5 10 12 15 17 20 23 25 27 30 33 35
0.4
0.5
0.6
0.7
0.8
0.9
Ridesharing recommendations
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Query RR(og,dt,time,mod)
Go.append (if gi in Circle(go,Dth)Gd.append (if gj in Circle(gd, Dth)
If RR.mod == public
Ro.append (if ri in GRT(Go ))Rd.append (if rj in GRT(Gd ))
RR.og->go
RR.dt->gd
Rp.append (if ri in Ro & ri in Rd)
RR.time
Rt.append (if ri in Rp & ri.st in [RR.st-SimTthreh, RR.st+SimTthreh]
No
Ro.append (if ri in GRT(Go)& ri.mod=driving)Rd.append (if rj in GRT(Gd )& rj.mod=driving)
Sort ri in Rt by ri.dtOr CR(ri)
Yes
Grid-Based Routes Table
g1(ri,…rj)g2(rm,…rn)
...
ExperimentsTesting Data (from Geolife project*)
178 users‘ real logs from 2007 to 201117+thousand trajectories48+ thousand hours1+ million kilometersthe majority of the data was created in Beijing, China.
* http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/default.aspx
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0 20 40 60 80 100 120 140 160 1800
500
1000
1500
2000
2500
0 20 40 60 80 100 120 140 160 1800
500
1000
1500
2000
2500
Participant Participant
Num
(a)The total number of original trajectories (b)The routes number of each user after routes extraction
Results on Regular route mining
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original trajectories regular route465 466 467 468 469 470 471 472
189
189.5
190
190.5
191
191.5
192
192.5
193
193.5
194
116.326116.328116.33116.332116.334116.336116.338116.34116.342116.344116.34639.994
39.996
39.998
40
40.002
40.004
40.006
40.008
original trajectories regular routes116.305 116.31 116.315 116.32 116.325 116.33 116.335 116.34
39.975
39.98
39.985
39.99
39.995
40
40.005
40.01
40.015
40.02
40.025
457 458 459 460 461 462 463 464 465 466 467182
184
186
188
190
192
194
196
198
Starting point
Ending point
All regular routes from Testing Data
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(a) Fthreh =3 (b) Fthreh =8
116 116.1 116.2 116.3 116.4 116.5 116.6 116.7 116.8 116.9 117
39.7
39.75
39.8
39.85
39.9
39.95
40
40.05
40.1
40.15
116 116.1 116.2 116.3 116.4 116.5 116.6 116.7 116.8 116.9 117
39.65
39.7
39.75
39.8
39.85
39.9
39.95
40
40.05
40.1
Some results in ridesharing recommendations
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116.3 116.32 116.34 116.36 116.38 116.4 116.42 116.4439.96
39.98
40
40.02
40.04
40.06
40.08
(a) (b)
116.28 116.3 116.32 116.34 116.36 116.38 116.4 116.42 116.4439.96
39.98
40
40.02
40.04
40.06
40.08
116.3 116.31 116.32 116.33 116.34 116.35 116.36 116.37 116.38 116.39 116.439.96
39.98
40
40.02
40.04
40.06
40.08
(C)
requester
requester
requester
(d) 116.32 116.34 116.36 116.38 116.4 116.42 116.44 116.46
39.93
39.94
39.95
39.96
39.97
39.98
39.99
40
requester
ConclusionA method for ridesharing recommendations
Finding more opportunities from GPS dataGiving more reliability of the rideProviding more information about the riders and the routes
An algorithm for Mining regular routesDistinguishing regular routes from frequent routes Calculating the similarity of a group of routes
A feature for Distinguish private driving and public transportation
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