Feasible study of a light weight prediction system in China

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



2

Prediction engine

Joint work with CenNavi Technologies Co.,Ltd*

Add-in for current working system (FCD based TIS)



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/

http://www.cennavi.com.cn/

Our target



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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



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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

0.8

0.85

0.9

0.95

1autocorrelation

gap(hour)

corr

ela

tion

correlation

0 1000 2000 3000 4000 5000 60000

10

20

30

40

50

60

70

0 100 200 300 400 500 600 700 800 900 10000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

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

0.5

0.6

0.7

0.8

0.9

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



6

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



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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



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Overall prediction evaluation

Each methods outperformed baseline predictor

FCD based link travel time data



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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



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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



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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



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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



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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.



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Feasible study of a light weight prediction system in China

Technology

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