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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

Traffic Incident Detection Using Probabilistic Topic Model

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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

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Page 1: Traffic Incident Detection Using Probabilistic Topic Model

Traffic Incident DetectionUsing

Probabilistic Topic Model

Akira Kinoshita1, Atsuhiro Takasu2, and Jun Adachi2

<[email protected]>

1The University of Tokyo2National Institute of Informatics, Japan

International Workshop on Mining Urban DataAthens, Greece, March 28, 2014

Page 2: Traffic Incident Detection Using Probabilistic Topic Model

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

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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

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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

+

Page 5: Traffic Incident Detection Using Probabilistic Topic Model

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

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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

Page 7: 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

Page 8: Traffic Incident Detection Using Probabilistic Topic Model

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Parameter Estimation

Most-likelihood estimation

2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model

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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

Page 10: Traffic Incident Detection Using Probabilistic Topic Model

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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

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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

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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

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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

Page 14: Traffic Incident Detection Using Probabilistic Topic Model

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

Page 15: Traffic Incident Detection Using Probabilistic Topic Model

15The Most Anomalous Trajectory

2014-03-28A. Kinoshita, A. Takasu, and J. Adachi.Traffic Incident Detection Using Probabilistic Topic Model

Accident

speed [km/h]

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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

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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

Page 18: 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

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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.

Page 20: Traffic Incident Detection Using Probabilistic Topic Model

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

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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.