t-drive : driving directions based on taxi trajectories microsoft research asia university of north...
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![Page 1: T-Drive : Driving Directions Based on Taxi Trajectories Microsoft Research Asia University of North Texas Jing Yuan, Yu Zheng, Chengyang Zhang, Xing Xie,](https://reader035.vdocument.in/reader035/viewer/2022062516/56649d2c5503460f94a020bd/html5/thumbnails/1.jpg)
T-Drive: Driving Directions Based on Taxi Trajectories
Microsoft Research AsiaUniversity of North Texas
Jing Yuan, Yu Zheng, Chengyang Zhang, Xing Xie, Guanzhong Sun, and Yan Huang
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What We Do
A smart driving direction service based on GPS traces of a large number of taxis
Find out the practically fastest driving directions with less online computation according to user queries
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t =7:00am
t = 8:30am
Q=( and t)
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Background
Shortest path and Fastest path (speed constraints)Real-time traffic analysis
MethodsRoad sensors Visual-based (camera) Floating car data
Open challenges: coverage, accuracy,…Have not been integrated into routing
Traffic light
parking
Human factor
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What a drive really needs?
Finding driving direction > > Traffic analysis
Background
Sensor Data
Traffic Estimation(Speed)
Driving Directions
Many open
challenges Error Propagation
Physical Routes Traffic flows Drivers
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Observations
A big city with traffic problem usually has many taxisBeijing has 70,000+ taxis with a GPS sensorSend (geo-position, time) to a management center
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MotivationTaxi drivers are experienced driversGPS-equipped taxis are mobile sensors
Human Intelligence Traffic patterns
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Challenges we are faced
Intelligence modeling Data sparseness Low-sampling-rate
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Pre-processing Building landmark graphEstimate travel timeTime-dependent two-stag routing
Methodology
A Time-dependent Landmark Graph
Taxi Trajectories
A Road Network
Rough Routing
Refined Routing
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Step 1: Pre-processing
Trajectory segmentationFind out effective trips with passengers inside a taxiA tag generated by a taxi meter
Map-matchingmap a GPS point to a road segmentIVMM method (accuracy 0.8, <3min)
e2e3.start
e3.endVi
Vj
R1 R2
R3
a
b
R4
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Step 2: Building landmark graphs
Detecting landmarksA landmark is a frequently-traversed road segmentTop k road segments, e.g. k=4
Establishing landmark edgesNumber of transitions between two landmark edges > E.g.,
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Tr1 r3
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r8
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A) Matched taxi trajectories B) Detected landmarks C) A landmark graph
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p3 p4
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Step 3: Travel time estimation
The travel time of an landmark edgeVaries in time of dayis not a Gaussian distributionLooks like a set of clusters
A time-based single valued function is not a good choice
Data sparsenessLoss information related to driversDifferent landmark edges have different time-variant patternsCannot use a predefined time splits
VE-ClusteringClustering samples according to varianceSplit the time line in terms of entropy
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Step 3: Travel time estimation
V-ClusteringSort the transitions by their travel timesFind the best split points on Y axis in a binary-recursive way
E-clusteringRepresent a transition with a cluster IDFind the best split points on X axis iteratively
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Step 4: Two-stage routing
Rough routingSearch a landmark graph forA rough route: a sequence of landmarksBased on a user query (, t, )Using a time-dependent routing algorithm
r4
r1
qd
0.1 r3
r2
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qs
C12(0.1)=2 C34(0.1)=1
0.1
C12(1.1)=1 C34(1.1)=2e12 e34
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Step 4: Two-stage routing
Refined routingFind out the fastest path connecting the consecutive landmarksCan use speed constraintsDynamic programming
Very efficientSmaller search spacesComputed in parallel
r4 r5r2qs qe
2 2 10.3 0.2
r4.end
r6
qe
r4.start r5.start
r5.endr2.end
r2.start r6.start
r6.end1.4
4.5 1.7
2.5
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A) A rough route
B) The refined routing
C) A fastest pathr2.end r4.end
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0.3
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Implementation & Evaluation
6-month real dataset of 30,000 taxis in BeijingTotal distance: almost 0.5 billion (446 million) KM Number of GPS points: almost 1 billion (855 million)Average time interval between two points is 2 minutesAverage distance between two GPS points is 600 meters
Evaluating landmark graphsEvaluating the suggested routes by
Using Synthetic queriesIn the field studies
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Evaluating landmark graphs
Estimate travel time with a landmark graphUsing real-user trajectories
30 users’ driving paths in 2montsGeoLife GPS trajectories (released)
K=2000 K=4000
K=500
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Evaluating landmark graphs
Accurately estimate the travel time of a route10 taxis/ is enough
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Synthetic queries
BaselinesSpeed-constraints-based method (SC)Real-time traffic-based method (RT)
MeasurementsFR1, FR2 and SRUsing SC method as a basis
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In the field studyEvaluation 1
Same drivers traverse different routes at different times
Evaluation 2Different two users with similar driving skillsTravers two routes simultaneously
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Results• More effective
• 60-70% of the routes suggested by our method are faster than Bing and Google Maps.• Over 50% of the routes are 20+% faster than Bing and Google. • On average, we save 5 minutes per 30 minutes driving trip.
• More efficient• More functional
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Thanks!
Yu ZhengMicrosoft Research Asia
A free dataset: GeoLife GPS trajectories160+ users in a period of 1+ years
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References
[1] Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, Yan Huang. T-Drive: Driving Directions Based on Taxi Trajectories. In Proceedings of ACM SIGSPATIAL Conference on Advances in Geographical Information Systems (ACM SIGSPATIAL GIS 2010).[2] Yin Lou, Chengyang Zhang*, Yu Zheng, Xing Xie. Map-Matching for Low-Sampling-Rate GPS Trajectories. In Proceedings of ACM SIGSPATIAL Conference on Geographical Information Systems (ACM SIGSPATIAL GIS 2009).[3] Jin Yuan, Yu Zheng. An Interactive Voting-based Map Matching Algorithm. In proceedings of the International Conference on Mobile Data Management 2010 (MDM 2010).