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.

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
Copyright (C) 2013 DENSO IT LABORATORY,INC.
All Rights Reserved.

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”
Copyright (C) 2013 DENSO IT LABORATORY,INC.
All Rights Reserved.
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.
All Rights Reserved.
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
Copyright (C) 2013 DENSO IT LABORATORY,INC.
All Rights Reserved.
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
Copyright (C) 2013 DENSO IT LABORATORY,INC.
All Rights Reserved.

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.
Copyright (C) 2013 DENSO IT LABORATORY,INC.
All Rights Reserved.

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
Copyright (C) 2013 DENSO IT LABORATORY,INC. All Rights Reserved.

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)
Copyright (C) 2013 DENSO IT LABORATORY,INC. All Rights Reserved.

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.
Copyright (C) 2013 DENSO IT LABORATORY,INC.
All Rights Reserved.
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
Copyright (C) 2013 DENSO IT LABORATORY,INC.
All Rights Reserved.

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
Copyright (C) 2013 DENSO IT LABORATORY,INC.
All Rights Reserved.

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
Copyright (C) 2013 DENSO IT LABORATORY,INC. All Rights Reserved.

16. 16
Thank you for your
attention !
Copyright (C) 2013 DENSO IT LABORATORY,INC.
All Rights Reserved.