Post on 12-Jun-2015
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Feasibility study of a light weight
traffic prediction in China2012.10.24 ITS World Congress 2012
Osamu Masutani @ Denso IT Laboratory, Inc.
Zheng Liu @ Denso Corporation
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
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Traffic Prediction
One of core technologies for Traffic Information System
(TIS)
Essential for medium to long range trip
Fundamental for dynamic activity scheduling
Compute intensive task
Huge amount of stream data
Needs performance consideration
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
2
Prediction engine
Joint work with CenNavi Technologies Co.,Ltd*
Add-in for current working system (FCD based TIS)
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
3
Link Travel Time
Generation
Real time
LTT
Model Training
Server-side DRG
Taxi-FCD
Bus-FCD
Infra-based
Sensing
Traffic Information System
Historical
LTT
Prediction methods
Short (Pheromone Model)
Middle (Clustered Pattern)
Long (Decision Tree)
Prediction Predicted LTT
Traffic Prediction Server
*http://www.cennavi.com.cn/
Our target
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
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Primary market : China
Excessive number of cars : heavy congestions
Acceptable accuracy for full-range prediction
Evaluation on short to long term predictions
Application-level evaluation
Secondary effect to enhances prediction
Compact and common architecture
Need less resource for high-coverage service
Need not special HW/SW
Accessible from normal user / developer
Multi-term predictions
Incremental prediction v.s. Statistical prediction
Cross over point real-time (incremental) correlation be fall below
daily correlation is around 1 hour
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
0.65
0.7
0.75
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0.85
0.9
0.95
1autocorrelation
gap(hour)
corr
ela
tion
correlation
0 1000 2000 3000 4000 5000 60000
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0 100 200 300 400 500 600 700 800 900 10000
0.1
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1自己相関: data
ラグ
Raw traffic data(Link travel time)
Long term correlation
Cross over between daily correlation andreal-time correlation
0 5 10 15 20 25 30 35 40 45 500
0.1
0.2
0.3
0.4
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1自己相関: data
ラグ
Short term correlation
Time gap
Au
toc
orr
ela
tio
n
Method 1 : short-term prediction
(5 min – 10min)
Pheromone model
Inspired by natural dynamics
Simple coupled map lattice
Discrete time, continuous value
Combination of spatial propagation and temporal decay
Already examined
Benchmark data in Mitaka 1996
FCD dataset in Nagoya 2004
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
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s
s s
s
),(),(),(),1( ptqptrptsEpts
r
)),()',(()(
),1()('
ptqptrpN
Fptq
pNp
qPropagation
Generation PropagationEvaporation
Neighbor links
Propagation parameter
Generation
Method 2 : Middle-term prediction
(10 min – 60min)
Prediction by most similar traffic pattern
Clustered pattern matching
Avoid exhaustive historical pattern search
10-20 clusters are enough to outperform exhaustive matching
Copyright (C) 2012 DENSO IT LABORATORY,INC.
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Method 3 : Long-term prediction
(60 min -)
Prediction by most similar day
pattern
Clustered daily traffic pattern
Euclid similarity
Relate cluster and date attribute
Attributes : day in a week, holiday,
weather, temperature
Trained by decision tree
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
8
Overall prediction evaluation
Each methods outperformed baseline predictor
FCD based link travel time data
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
9
Short-term Mid/Long term
Location 3rd ring in
Beijing
Outer ring highway in
Shanghai
# of links 397 55
length 94 km 26km
Test duration 1 week 2 week
RM
SE(L
ow
er
is b
ett
er)
Optimal method for each horizon
Middle term prediction
Apply our method to its advantageous range
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
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PersistentARSimple patternClustered pattern
Application level evaluation
Arrival time prediction (ATP)
One of most popular usage of traffic prediction
Our methods outperforms ATP using current traffic only
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
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9.04
6.3
0123456789
10
Current only Decision Tree
5.54 5.29
0
1
2
3
4
5
6
Current only Clustered pattern
RM
SE(L
ow
er
is b
ett
er)
Complementation effect for unknown
data
Unknown data in FCD based link travel time
Very sparse in unpopulated area
Spatial or temporal complementation
Evaluation
Better than “copy” of
current data
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
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4.36 4.14
0
1
2
3
4
5
Current Prediction
4.47 4.23
0
1
2
3
4
5
Current Prediction
2.1 3.2 5.5
3.0 N/A 4.2
N/A N/A 3.0
Links
Tim
e
2.1 3.2 5.5
3.0 N/A 4.2
N/A N/A 3.0
Links
Tim
e
Temporal comp. Spatial comp.
Need less resource for high-coverage
prediction
Prediction for all navigation links
Massive links (150k in Beijing) >> traffic links
Highly required to find byway during congestion
Need not special H/W S/W
5 years ago processor is enough
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traffic links
navigation links
Short-term Intermediate-termSpec of PC CPU: Xeon E5410 (Quad core 2.3GHz) Memory: 4GB
OS: Windows Server 2008 R2 x64# of links 150,000linksTotal processing time onsingle core
2.6 sec 45.4 sec
Total processing time onquad core
0.7 sec 11.3 sec
System architecture
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
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Common technology
Windows Server
SQL Server
ArcGIS (shape file)
Easy maintenance
C# .NET managed code
Log & alert system by Windows Server
Prediction evaluation & analysis by PowerPivot (data cube)
Summary
Acceptable accuracy for full-range prediction
Evaluation on real traffic data in China
All of evaluations confirmed better accuracy than baseline
Compact and common architecture
Common architecture is enough to implement large area prediction
Common architecture provide low-cost, less needs of skill, high usability, robust system without scratch built code.
Copyright (C) 2012 DENSO IT LABORATORY,INC.
All Rights Reserved.
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