toward a resilient prediction system for non-uniform traffic data
DESCRIPTION
We developed a traffic prediction system which enhances a traffic information service. The prediction method is based on time series analysis and is applicable to short to long term prediction. Traffic information system are real-time and real-world system therefore it suffers various kind of disturbance from environment. To preserve traffic prediction quality, we need fundamental treatment on overall system so that the prediction engine be tolerant toward incomplete traffic data feed or non-stationary traffic data. A solution for incomplete data feed is a combination of data for multiple links. A solution for non-stationary traffic is a traffic simulation dedicated to traffic accidents. With these enhancements toward cyber disturbance and physical disturbance, the system resiliency can be higher.TRANSCRIPT
1
Toward a resilient prediction system for non-uniform traffic data2013.10.18 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 Resilient city
Important characteristics of smart city
City system should be resilient against : Natural disaster
Unusual weather
Any accident
Extraordinary social event
Copyright (C) 2013 DENSO IT LABORATORY,INC.All Rights Reserved.
“resilient city”“resilient system”
Google trend
20132009
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 Resilient Traffic Information System
Cyber-physical loop which provides resilience of city.
TIS itself suffers various cyber / physical disturbances
Copyright (C) 2013 DENSO IT LABORATORY,INC.All Rights Reserved.
TrafficSensor
TrafficControl
TrafficPredictionSystem
FailureCyberAttack
NaturalDisaster
UnusualEvent
CITY
Cyber
Physical
Copyright (C) 2013 DENSO IT LABORATORY,INC. All Rights Reserved.
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
Link Travel Time Generation
Real timeLTT
Model Training
Server-side DRG
Taxi-FCD
Bus-FCD
Infra-basedSensing
Traffic Information System
HistoricalLTT
Prediction methods
Short (Pheromone Model)
Middle (Clustered Pattern)
Long (Decision Tree)
Prediction Predicted LTT
Traffic Prediction Server
*http://www.cennavi.com.cn/
Copyright (C) 2013 DENSO IT LABORATORY,INC. All Rights Reserved.
6 Motivation
Primary target : China : disturbance is potentially large
Physical disturbance : congestion , heavy smog , social event
Cyber disturbance : absence of FCD , communication error
CurrentSystem
Traffic Simulation
Link merge
Physical (traffic) disturbance
Cyb
er
(data
) dis
turb
an
ce
Web news site : Zenshinhttp://www.zenshin-s.org/zenshin-s/sokuhou/2011/10/post-1328.html
Cennavi : in-vehicle navigationhttp://cennavi.com.cn/en/Product/page.php?id=82&pid=57
Our extensions
Copyright (C) 2013 DENSO IT LABORATORY,INC. All Rights Reserved.
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
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Multi-link multi-time delay NB 2-4 neighbor links 5 steps delay
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 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.
Copyright (C) 2013 DENSO IT LABORATORY,INC. All Rights Reserved.
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 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)
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
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
Lane closed by accident
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 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.
Copyright (C) 2013 DENSO IT LABORATORY,INC. All Rights Reserved.
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
16
Thank you for your attention !
Copyright (C) 2013 DENSO IT LABORATORY,INC.All Rights Reserved.