This document discusses developing a resilient traffic prediction system to handle nonuniform traffic data. It proposes using link merge to complement missing data through neighboring link data. An evaluation shows this approach improves accuracy over baseline methods. It also proposes using hybrid traffic simulation to predict impacts of unusual traffic events like accidents by balancing detail and performance. The system aims to make traffic prediction, a key part of resilient city infrastructure, more robust against various cyber and physical disturbances.
Toward a resilient prediction system for non-uniform traffic data
1. 1
Toward a resilient
prediction system for nonuniform traffic data
2013.10.18 ITS World Congress 2013
Osamu Masutani @ Denso IT Laboratory, Inc.
Zheng Liu @ Denso Corporation
Tomio Miwa, Takayuki Morikawa @ Nagoya University
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2. 2
Resilient city
Important characteristics of
smart city
City system should be resilient
against :
Natural disaster
Unusual weather
Any accident
Google trend
Extraordinary social event
“resilient city”
“resilient system”
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2009
2013
3. 3
Traffic information system for
resilient city
One of important system for
resilient city against disaster
Right navigation for escape
or emergency logistics
We can say traffic
information system can save
people
Copyright (C) 2013 DENSO IT LABORATORY,INC.
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Passable Road Confirmation Map
@ East Japan quake.
4. 4
Resilient Traffic Information
System
Cyber-physical loop which provides resilience of city.
TIS itself suffers various cyber / physical disturbances
Unusual
Event
Natural
Disaster
CITY
Physical
Cyber
System
Failure
Traffic
Sensor
Traffic
Control
Traffic
Prediction
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Cyber
Attack
5. 5
Our system
Traffic prediction system based on floating car data
Joint work with CenNavi Technologies Co.,Ltd*
Mainly for usual traffic because the methods are based on historical data
Traffic Information System
Traffic Prediction Server
Link Travel Time
Generation
Prediction
Real time
LTT
Short (Pheromone Model)
Predicted LTT
Taxi-FCD
Bus-FCD
Historical
LTT
Middle (Clustered Pattern)
Long (Decision Tree)
Server-side DRG
Prediction methods
Infra-based
Sensing
Model Training
Copyright (C) 2013 DENSO IT LABORATORY,INC. All Rights Reserved.
*http://www.cennavi.com.cn/
6. Motivation
Primary target : China : disturbance is potentially large
Physical disturbance : congestion , heavy smog , social event
Cyber disturbance : absence of FCD , communication error
Cyber (data) disturbance
6
Link merge
Our extensions
Web news site : Zenshin
http://www.zenshin-s.org/zenshin-s/sokuhou/2011/10/post-1328.html
Current
System
Traffic
Simulation
Physical (traffic) disturbance
Copyright (C) 2013 DENSO IT LABORATORY,INC. All Rights Reserved.
Cennavi : in-vehicle navigation
http://cennavi.com.cn/en/Product/page.php?id=82&pid=57
7. 7
Data complementation with
link merge
Unknown data caused by FCD
Should be complemented before prediction
Using surrounding link data
Prediction based complementation
Naïve Bayes model
Doesn’t require full input data
Multi-link multi-time delay NB
2-4 neighbor links
5 steps delay
?
?
?
?
Copyright (C) 2013 DENSO IT LABORATORY,INC. All Rights Reserved.
8. 8
Evaluation
Specification
Travel time (speed) data
North part of Beijing outer 4th ring
15 links, 20km
Compare our Naïve Bayes
complementation with baseline
complementation
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9. 9
Link combination
How far links we should
employ from surroundings
Relevance matrix
Each cell represents
combination of links
Cell value represent
difference of prediction
error with singular link
Blue cell means better
prediction than singular
link
Direct neighbor link is
always improve
accuracy.
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10. 10
Complementation scheme
by combination of links
Unknown data slot is complemented
Evaluation spec:
Artificially omitted data that have certain interval of
absence of data
Use neighbor 2 links (upstream and downstream)
Evaluation index : MAPE of travel time
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11. 11
Evaluation result
Prediction outperforms baseline complementation
Base line : Persistent (copy) comp. , Statistical comp.
80% better accuracy than others with 24 steps absence
of data (2 hours)
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12. 12
Traffic simulation
Unusual traffic
Current prediction engine cannot predict
For prediction for unknown situation caused mainly by accident
we employ traffic simulation
Hybrid simulation
Balance detail and performance
1) QV curve estimation
Lane closed by accident
2) Queue-based microscopic model
Both are performed on each lane so
it can potentially estimate impact of
a lane closure.
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http://blog.livedoor.jp/colt3/archives/876394.html
13. 13
Methodology
Separate queuing part and moving part
For moving part we use QV curve derived by traffic
sensor data for each lane
For queuing part we apply queue based simulation for
each lane
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14. 14
Current status
Simulation is conducted in Shanghai
Evaluated with city-wide highway traffic sensor data
Applied to normal traffic
Correlation coefficient with observed traffic volume is
0.88
Future work
Irregular traffic
Local road
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15. 15
Summary
Resilient city should have resilient traffic information
system
Traffic prediction is one of important feature for resilience
Traffic prediction itself suffered by various disturbance
Unusual system behavior (data lost, communication error … )
Unusual traffic (accident , heavy weather …)
Our new traffic prediction system employ
Link merge to tackle unusual system behavior
Hybrid traffic simulation to tackle unusual traffic
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16. 16
Thank you for your
attention !
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All Rights Reserved.