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Traffic Incident Detection Using Probabilistic Topic Model. Akira Kinoshita 1 , Atsuhiro Takasu 2 , and Jun Adachi 2 < [email protected] > 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
Citation preview
Traffic Incident DetectionUsing
Probabilistic Topic Model
Akira Kinoshita1, Atsuhiro Takasu2, and Jun Adachi2
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.