Traffic Incident Detection Using Probabilistic Topic Model

1. Traffic Incident DetectionUsingProbabilistic Topic Model Akira Kinoshita1, Atsuhiro Takasu2, and Jun Adachi2 <kinoshita@nii.ac.jp> 1The University of Tokyo 2National Institute of Informatics, Japan International Workshop on Mining Urban Data Athens, 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 Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

3. Background 東池袋 Higashi-Ikebukuro Causes of congestion in Japan [E-NEXCO] Probe-car data (position+timestamp) 護国寺 Gokokuji • Local slowdown • Incident？ http://www.e-nexco.co.jp/english/business_activities/expressway_management/eliminating.html 早稲田 Waseda Congestion≠Incident Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

4. Related Work [Zhu, et al. 2009] Date Link Feature vector based on the speed change − slowdown@incident + Time • Distance-basedoutlier detectionafter filterling slow slow fast Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

5. Research 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 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 Experiment Using 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 Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

7. Traffic State Model(TSM) Similar to LDA [Blei 2003] Good Moderate Congested Stop ⇔Topic ・・・ Traffic States ⇔Word Observed Value ⇔document Inbound artery, weekday morning Segment Inbound expressway, midnight Distribution of is mixture distribution Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

8. Parameter Estimation Most-likelihood estimation Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

9. Incident Detection Method G: Good C: Congested M: Moderate S: Stop G M C S G G G G G G Usual Traffic State Divergence between usual - current states  measure by KL divergence Incident Current Traffic State G M C S G C M C S G Observed Value Divergence  sum of divs Segment Toll gate Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

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 Experiment Using 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 Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

11. Experiment Purpose To 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 Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

12. Dataset & Preprocessing Preprocessing 1. Map matching 2. Trajectory identification 3. Interpolation 4. Labeling Parameter assumptions • K = 8 • N = 10 • Poisson distribution Area Map [ShutoExpwy.] (5)Ikebukuro Segment = 50-m length section Central Tokyo (4)Shinjuku (3)Shibuya Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

13. Parameter Estimation Poisson mixture at a certain segment in the inbound Shibuya route Estimated Poisson mixture fits the original histogram Component Poisson multiplied by coefficient Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

14. Incident DetectionResult – ROC curve outbound (best) outbound (worst) better Area Under the Curve Proposed: 0.812 Baseline: 0.794 Baseline: [Zhu, et al. 2009] Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

15. The Most Anomalous Trajectory speed [km/h] Accident Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

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 Experiment Using 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 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 Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

18. Road Characteristic Analysis Based on TSM Fastest state Usually slow section Slowest state Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

19. Conclusions • Traffic state model Mixing shared states in different ratio for each segment • Detection method Compare current/usual traffic states  find irregular events • Experimental result Better detection performance than existing method using real probe-car data in Shutoko • Possible applications of traffic state model Traffic characteristic analysis ・・・ Usual G M C S G G G G G G G M C S G C M C S G Cur. Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model

20. Some 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 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 method Compare current/usual traffic states  find irregular events • Experimental result Better detection performance than existing method using real probe-car data in Shutoko • Possible applications of traffic state model Traffic characteristic analysis ・・・ Usual G M C S G G G G G G G M C S G C M C S G Cur. Kinoshita, A. Takasu, and J. Adachi. Traffic Incident Detection Using Probabilistic Topic Model