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

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

t =7:00am

t = 8:30am

Q=( and t)

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

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

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

MotivationTaxi drivers are experienced driversGPS-equipped taxis are mobile sensors

Human Intelligence Traffic patterns

Challenges we are faced

Intelligence modeling Data sparseness Low-sampling-rate

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

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

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.,

r2

Tr1 r3

r9

r8

r6

r1

Tr2

Tr5

Tr3

Tr4

A) Matched taxi trajectories B) Detected landmarks C) A landmark graph

r9

r3r1

r6

r9

r3r1

r6

p1 p2

p3 p4

r4

r5r7

r10

e16

e96

e93

e13

e63

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

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

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

0.1

0.1

qs

C12(0.1)=2 C34(0.1)=1

0.1

C12(1.1)=1 C34(1.1)=2e12 e34

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

2.8

2.4

3.2

0.9

qe1.4

2.5

0.9

r2.start

A) A rough route

B) The refined routing

C) A fastest pathr2.end r4.end

r4.start r5.start

r5.end

r6.start

r6.end

0.3

0.2

0.3

0.2

1 1 1 1

1 11 1

qs

qs

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

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

Evaluating landmark graphs

Accurately estimate the travel time of a route10 taxis/ is enough

Synthetic queries

BaselinesSpeed-constraints-based method (SC)Real-time traffic-based method (RT)

MeasurementsFR1, FR2 and SRUsing SC method as a basis

In the field studyEvaluation 1

Same drivers traverse different routes at different times

Evaluation 2Different two users with similar driving skillsTravers two routes simultaneously

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

Thanks!

Yu ZhengMicrosoft Research Asia

A free dataset: GeoLife GPS trajectories160+ users in a period of 1+ years

yuzheng@microsoft.com

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).