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Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
Toward a resilient prediction system for non-uniform traffic data
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Toward a resilient prediction system for non-uniform traffic data

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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. …

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.

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  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.

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