T-Drive:Driving Directions Based on Taxi Trajectories
Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun and Yan Huang
Microsoft Research, Computer science department, University of North Texas
2010
Presented by Salem OthmanKent state university
Nov-4-2011
Email: [email protected]
http://www.samtaxicabservices.com/
Background
How long does it really take to drive from point A to point B at 5:00 pm?
Shortest Time.
Shortest Distance.
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Background Cont.
Practically fastest route
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Motivation
Big cities have a large number of taxicabs equipped with a GPS sensors
Historical GPS trajectories
Taxi drivers are experienced drivers
http://barrycarguythomas.blogspot.com/2011/05/monday-another-taxi-story.html 4
Goal
Model the dynamic traffic patterns
Model intelligence of experienced drivers
http://www.fastcompany.com/1644403/microsoft-predestination-can-predict-where-youre-goinghttp://www.asnowtech.com/genetics-of-human-intelligence-2171215.html
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Outline
Challenges faced performing the system
Methodology
Trajectory preprocessing
Landmark graph construction
Travel time estimation
Route computing
Experiments
Conclusion
References
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Challenges faced performing the system
Intelligence modeling
Can we answer any user query?
Data sparseness and coverage
Can we accurately estimate the speed pattern of each road segment?
Low sampling rate problem
Is there uncertainty of the routes traversed by a taxi?
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Outline
Challenges faced performing the system
Methodology
Trajectory preprocessing
Landmark graph construction
Travel time estimation
Route computing
Experiments
Conclusion
References
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Step 1: Preprocessing
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Taxi trajectory: a sequence of GPS points pertaining to one trip.
Road segment: a directed edge, one-way or bidirectional
Trajectory segmentation
Partition a GPS log into some taxi trajectories
Map matching
Map each GPS point of a trip to the corresponding segment
Taxi #6, 3 Days
R1 R2
R3
a
b
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Outline
Challenges faced performing the system
Methodology
Trajectory preprocessing
Landmark graph construction
Travel time estimation
Route computing
Experiments
Conclusion
References
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Step 2: Building the landmark graph
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Landmark: one of the top-k road segment being frequently traversed by taxis
Select top-k road segments
Connect two landmarks with a landmark edge
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Tr1 r3
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Tr2
Tr5
Tr3
Tr4
A) Matched taxi trajectories B) Detected landmarks C) A landmark graph
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Outline
Challenges faced performing the system
Methodology
Trajectory preprocessing
Landmark graph construction
Travel time estimation
Route computing
Experiments
Conclusion
References
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Step 3: Travel time estimation
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Travel time gather around some values like a set of clusters.
V-clustering
Find the best split point having minimal weighted average variance
E-clustering
Split the x-axis into several time slots
Compute the distribution of travel time in each time slot
Outline
Challenges faced performing the system
Methodology
Trajectory preprocessing
Landmark graph construction
Travel time estimation
Route computing
Experiments
Conclusion
References
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Step 4: Route computing
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Rough routing
In landmark graph
Search m nearest landmarks for source and destination points.
For each pair of landmark find time-dependent fastest route.
Refined routing
In real road network
Dynamic programming
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r5.endr2.end
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4.5 1.7
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A) A rough route
B) The refined routing
C) A fastest pathr2.end r4.end
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r5.end
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A Time-dependent Landmark Graph
Taxi Trajectories
A Road Network
Rough Routing
Refined Routing
Outline
Challenges faced performing the system
Methodology
Trajectory preprocessing
Landmark graph construction
Travel time estimation
Route computing
Experiments
Conclusion
References
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Experiments
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Data
Road network of Beijing has 106,579 nodes and 141,380 segments
Taxi trajectories 33,000 taxis over 3 months total distance 400 million Km total GPS points 790 million
The average interval is 3.1, average distance 600 meters, 4.96 million trajectories
Evaluation framework
Landmark graph
Based on synthetic queries
In-the-field
K=500 K=4000
Outline
Challenges faced performing the system
Methodology
Trajectory preprocessing
Landmark graph construction
Travel time estimation
Route computing
Experiments
Conclusion
References
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Conclusion
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60-70% of the routes suggested are faster than the competing methods
20% of the routes share the same results
On average, 50% of routes are at least 20% faster than the competing approaches
Outline
Challenges faced performing the system
Methodology
Trajectory preprocessing
Landmark graph construction
Travel time estimation
Route computing
Experiments
Conclusion
References
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References
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[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).
Thank you
