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Traffic Incident Detection Using Probabilistic Topic Model. Akira Kinoshita 1 , Atsuhiro Takasu 2 , and Jun Adachi 2 < kinoshita@nii.ac.jp > 1 The University of Tokyo 2 National Institute of Informatics, Japan International Workshop on Mining Urban Data Athens, Greece, March 28, 2014. - PowerPoint PPT Presentation
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Traffic Incident DetectionUsing
Probabilistic Topic Model
Akira Kinoshita1, Atsuhiro Takasu2, and Jun Adachi2
<kinoshita@nii.ac.jp>
1The University of Tokyo2National Institute of Informatics, Japan
International Workshop on Mining Urban DataAthens, Greece, March 28, 2014
2
Abstract
• Detect traffic incidents by comparing current traffic with usual traffic with less knowledge of traffic engineering.
• Show the method works well on the Shuto Expressway in Tokyo, using real probe-car data
• Possible by-product: characteristic analysis of road
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
3
Background
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
東池袋Higashi-
Ikebukuro
護国寺Gokokuji
早稲田Waseda
Local slowdown Incident?
Causes of congestionin Japan [E-NEXCO]
http://www.e-nexco.co.jp/english/business_activities/expressway_management/eliminating.html
Probe-car data(position+timestamp)
Congestion≠Incident
4
Related Work
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
[Zhu, et al. 2009]
Time
Link
slowdown@inciden
t
Date
slow slow fast
Feature vectorbased on the speed change
Distance-basedoutlier detectionafter filterling
+
−
5Research Problem& Solution Strategy
Problem• Congestion is not always caused by traffic incidents
speed reduction often occurs without any incidents
• Real-time data stream processing
Solution strategy• Regard incident as “sudden event different from usual”
• Estimate traffic state using probe-car data (or else)
• Compare current traffic with usual traffic current traffic should be much different from usual
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
6
Outline
Background• Automatic incident detection• Related work & research problem
Methodology• Traffic state model based on topic model• Estimate usual/current traffic states and then compare
them
ExperimentUsing probe-car data on three routes of the Shuto Expressway in Tokyo during 2011
Discussion• Results of the experiment• Possible analysis using traffic state model
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
7Traffic State Model (TSM) Similar to LDA [Blei 2003]
Distribution of is mixture distribution
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
Inbound expressway,midnight
Inbound artery,weekday morning
TrafficStates
Segment
ObservedValue
⇔Topic
⇔Word
⇔document
・・・
Congested StopModerateGood
8
Parameter Estimation
Most-likelihood estimation
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
9
Incident Detection Method
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
G M C S G C M C S G
G M C S G G G G G GUsualTraffic State
CurrentTraffic State
ObservedValue
Segment
G: GoodC: Congested
M: ModerateS: Stop
Incident
Divergence sum of divs
Toll gate
Divergence betweenusual - current states
measure by KL divergence
10
Outline
Background• Automatic incident detection• Related work & research problem
Methodology• Traffic state model based on topic model• Estimate usual/current traffic states and then compare
them
ExperimentUsing probe-car data on three routes of the Shuto Expressway in Tokyo during 2011
Discussion• Results of the experiment• Possible analysis using traffic state model
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
11
Experiment
PurposeTo show the proposed method works more efficiently than an existing method.
Data Sources• Probe-car data in the Shuto Expressway (Shutoko) in
Tokyo during 2011.Timestamp, position (longitude, latitude),vehicular speed (non-negative integer)
• Traffic log by the road administrator as the ground truth of incidents.
“Incident” includes the five events:accident, broken-down car, fallen object,
construction, looking-aside driving
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
12
Dataset & Preprocessing
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
Preprocessing1. Map matching2. Trajectory identification3. Interpolation4. Labeling
Parameter assumptions• K = 8• N = 10• Poisson distribution
Area Map[Shuto Expwy.]
(5)Ikebukuro
(4)Shinjuku
(3)Shibuya
CentralTokyo
Shuto Expwy.Routes
(3)Shibuya (4)Shinjuku (5)Ikebukuro
Inbound Out- In- Out- In- Out-
Period 1 Jan 2011 – 31 Dec 2011 (365 days)
# of cars
Total 100,581 95,386 95,293 88,345 128,789 114,942
Anomaly
4,259 2,475 4,365 3,891 6,089 5,603
Segment= 50-m length section
13
Parameter Estimation
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
Poisson mixture at a certain segment in the inbound Shibuya route
Estimated Poisson mixture fits the original histogram
Component Poisson multiplied by coefficient
14Incident DetectionResult – ROC curve
outbound (best) outbound (worst)
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
Baseline: [Zhu, et al. 2009]
better
Area Under the CurveProposed: 0.812Baseline: 0.794
15The Most Anomalous Trajectory
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
Accident
speed [km/h]
16
Outline
Background• Automatic incident detection• Related work & research problem
Methodology• Traffic state model based on topic model• Estimate usual/current traffic states and then compare
them
ExperimentUsing probe-car data on three routes of the Shuto Expressway in Tokyo during 2011
Discussion• Results of the experiment• Possible analysis using traffic state model
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
17
Discussion
• False Positives• Our definition of “incident” also matches abnormally fast
cars• Deeper analysis on alarmed trajectories is needed
• Proposed method can detect anomalies in real time• Learn traffic state model and compute divergence matrix in
advance• Use sliding window for each probe car
• Our approach performed better than an existing method based on physical traffic model• Congestion often occurs without any incidents on Shutoko;
there are many bottlenecks• Finding difference between current and usual traffic states
has an effect on traffic incident detection
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
18Road Characteristic Analysis Based on TSM
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
Fastest state
Slowest state
Usually slow section
19
Conclusions• Traffic state model
Mixing shared states in different ratio for each segment
• Detection methodCompare current/usual traffic states find irregular events
• Experimental resultBetter detection performance than existing method using real probe-car data in Shutoko
• Possible applications of traffic state modelTraffic characteristic analysis
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
・・・
G M C S G C M C S G
G M C S G G G G G GUsual
Cur.
20Some of Future Work(Working Now)
• (Automatic) Parameter tuning• K: the number of traffic states• N: the length of sliding window
• Proper divergence function selection• More deep analysis on anomalous
trajectories
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
21
Thank you for listening• Traffic state model
Mixing shared states in different ratio for each segment
• Detection methodCompare current/usual traffic states find irregular events
• Experimental resultBetter detection performance than existing method using real probe-car data in Shutoko
• Possible applications of traffic state modelTraffic characteristic analysis
2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model
・・・
G M C S G C M C S G
G M C S G G G G G GUsual
Cur.
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