Real-time traffic incident detection using a probabilistic topic model

Traffic congestion occurs frequently in urban settings, and is not always caused by traffic incidents. In this paper, we propose a simple method for detecting traffic incidents from probe-car data by identifying unusual events that distinguish incidents from spontaneous congestion. First, we introduce a traffic state model based on a probabilistic topic model to describe the traffic states for a variety of roads. Formulas for estimating the model parameters are derived, so that the model of usual traffic can be learned using an expectation-maximization algorithm. Next, we propose several divergence functions to evaluate differences between the current and usual traffic states and streaming algorithms that detect high-divergence segments in real time. We conducted an experiment with data collected for the entire Shuto Expressway system in Tokyo during 2010 and 2011. The results showed that our method discriminates successfully between anomalous car trajectories and the more usual, slowly moving traffic patterns.

[1]  Suttipong Thajchayapong,et al.  A new system for traffic incident detection using fuzzy logic and majority voting , 2013, 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[2]  B.S. Kerner,et al.  Traffic state detection with floating car data in road networks , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[3]  Muhammad Ali Imran,et al.  Anomaly Detection in Wireless Sensor Networks in a Non-Stationary Environment , 2014, IEEE Communications Surveys & Tutorials.

[4]  Saul Rodriguez,et al.  Real-Time Urban Traffic State Estimation with A-GPS Mobile Phones as Probes , 2012 .

[5]  Fang Yuan,et al.  INCIDENT DETECTION USING SUPPORT VECTOR MACHINES , 2003 .

[6]  Gaetano Valenti,et al.  Traffic Estimation And Prediction Based On Real Time Floating Car Data , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[7]  Yoan Miche,et al.  Mixture Models and EM , 2007 .

[8]  Rose Yu,et al.  GLAD: group anomaly detection in social media analysis , 2014, ACM Trans. Knowl. Discov. Data.

[9]  Tanja Zseby,et al.  Analysis of network traffic features for anomaly detection , 2014, Machine Learning.

[10]  Serge P. Hoogendoorn,et al.  Real-Time Lagrangian Traffic State Estimator for Freeways , 2012, IEEE Transactions on Intelligent Transportation Systems.

[11]  Shaogang Gong,et al.  A Markov Clustering Topic Model for mining behaviour in video , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  Zhongsheng Hua,et al.  A Hybrid Approach for Automatic Incident Detection , 2013, IEEE Transactions on Intelligent Transportation Systems.

[13]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[14]  Yizhou Yu,et al.  Anomaly detection in GPS data based on visual analytics , 2010, 2010 IEEE Symposium on Visual Analytics Science and Technology.

[15]  Suttipong Thajchayapong,et al.  Traffic incident detection system using series of point detectors , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[16]  Atsuhiro Takasu,et al.  Traffic Incident Detection Using Probabilistic Topic Model , 2014, EDBT/ICDT Workshops.

[17]  Jin Young Choi,et al.  Two-stage online inference model for traffic pattern analysis and anomaly detection , 2014, Machine Vision and Applications.

[18]  Atsuhiro Takasu,et al.  Real-time traffic incident detection using probe-car data on the Tokyo Metropolitan Expressway , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[19]  J M Sussman.  ITS: A SHORT HISTORY AND PERSPECTIVE ON THE FUTURE , 1996 .

[20]  Marimuthu Palaniswami,et al.  Smart car parking: Temporal clustering and anomaly detection in urban car parking , 2014, 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).

[21]  Wei Huang,et al.  A clustering approach to online freeway traffic state identification using ITS data , 2010 .

[22]  Lin Shang,et al.  Anomaly detection in crowded scenes using genetic programming , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[23]  Baher Abdulhai,et al.  Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network , 1999 .

[24]  Daniel Gatica-Perez,et al.  Discovering routines from large-scale human locations using probabilistic topic models , 2011, TIST.

[25]  Jae-Gil Lee,et al.  Trajectory Outlier Detection: A Partition-and-Detect Framework , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[26]  Alessandro Perina,et al.  Analyzing Tracklets for the Detection of Abnormal Crowd Behavior , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[27]  Zhibin Zhao,et al.  EM-LDA model of user behavior detection for energy efficiency , 2014, 2014 IEEE International Conference on System Science and Engineering (ICSSE).

[28]  Weifeng Lv,et al.  Outlier mining based Automatic Incident Detection on urban arterial road , 2009, Mobility Conference.

[29]  Shiqiang Hu,et al.  Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes , 2014, TheScientificWorldJournal.

[30]  Jaimyoung Kwon.  Modeling Freeway Traffic with Coupled HMMs , 2000 .

[31]  Jianhua Guo,et al.  Real time traffic flow outlier detection using short-term traffic conditional variance prediction , 2015 .

[32]  Robert B. Noland,et al.  Current map-matching algorithms for transport applications: State-of-the art and future research directions , 2007 .

[33]  David B. Dunson,et al.  Probabilistic topic models , 2011, KDD '11 Tutorials.

[34]  Atsuhiro Takasu,et al.  HIGHWAY INCIDENT DETECTION BASED ON PROBE CAR DATA , 2013, ICIS 2013.

[35]  David Bernstein,et al.  Some map matching algorithms for personal navigation assistants , 2000 .

[36]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[37]  Xiuzhen Zhang,et al.  Anomaly detection in online social networks , 2014, Soc. Networks.

[38]  Mingyan Liu,et al.  Surface street traffic estimation , 2007, MobiSys '07.

[39]  Amitabha Mukerjee,et al.  Anomaly Localization in Topic-Based Analysis of Surveillance Videos , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[40]  Gian Luca Foresti,et al.  Trajectory-Based Anomalous Event Detection , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[41]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[42]  Jaeseok Yang,et al.  THE MAP MATCHING ALGORITHM OF GPS DATA WITH RELATIVELY LONG POLLING TIME INTERVALS , 2005 .

[43]  A. Akobeng,et al.  Understanding diagnostic tests 3: receiver operating characteristic curves , 2007, Acta paediatrica.

[44]  Alexandre M. Bayen,et al.  Estimating arterial traffic conditions using sparse probe data , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[45]  T.Y. Lin,et al.  Anomaly detection , 1994, Proceedings New Security Paradigms Workshop.

[46]  Martin Treiber,et al.  Traffic Flow Dynamics , 2013 .

[47]  Sherif Ishak,et al.  A Hidden Markov Model for short term prediction of traffic conditions on freeways , 2014 .