Comparison of clustering techniques for traffic accident detection

Transportation infrastructure in intelligent transportation systems (ITSs) is complemented with information and communication technologies to achieve better passenger safety and reduced transportation time, fuel consumption, and vehicle wear and tear. This paper shows how data mining techniques are used in ITSs for accident detection and prevention on motorways. In traffic, vehicles show similar behavior to that of vehicles in closed neighborhoods. Vehicles that show different behaviors than neighbor vehicles in cases like accidents, inappropriate lane changes, and speeding can be considered as anomalies and detected. In this paper, a traffic accident is simulated and the effectiveness of different clustering techniques is examined for detecting traffic accident. We show that if velocity and position values of each vehicle are given, vehicles' behavior can be analyzed and accidents can be detected easily. The success of the proposed algorithms is demonstrated in a highway scenario by means of simulations. Simulations showed that data mining tools successfully detected accidents with an average accident detection rate of 100% and a false alarm rate of 0% using DBSCAN and hierarchical clustering.

[1]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[2]  Osama Abu Abbas,et al.  Comparisons Between Data Clustering Algorithms , 2008, Int. Arab J. Inf. Technol..

[3]  Matthew Rizzo,et al.  Unsafe rear-end collision avoidance in Alzheimer's disease , 2006, Journal of the Neurological Sciences.

[4]  Azzedine Boukerche,et al.  Vehicular Ad Hoc Networks: A New Challenge for Localization-Based Systems , 2008, Comput. Commun..

[5]  Hironao Kawashima,et al.  Japanese perspective of driver information systems , 1990 .

[6]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[7]  Saïd Mammar,et al.  Wireless Local Danger Warning: Cooperative Foresighted Driving Using Intervehicle Communication , 2010, IEEE Transactions on Intelligent Transportation Systems.

[8]  Dipak Ghosal,et al.  Smoothing Vehicular Traffic Flow Using Vehicular-Based Ad Hoc Networking & Computing Grid (VGrid) , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[9]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[10]  Javier A. Barria,et al.  Anomaly Detection using Microscopic Traffic Variables on Freeway Segments , 2010 .

[11]  Li-Yen Chang,et al.  Analysis of traffic injury severity: an application of non-parametric classification tree techniques. , 2006, Accident; analysis and prevention.

[12]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[13]  David Shinar,et al.  Imperfect in-vehicle collision avoidance warning systems can aid distracted drivers , 2007 .

[14]  Massimo Coppola,et al.  Experiments in Parallel Clustering with DBSCAN , 2001, Euro-Par.

[15]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[16]  J. Vermunt,et al.  Latent class cluster analysis , 2002 .

[17]  Raja Sengupta,et al.  Cooperative Collision Warning Systems: Concept Definition and Experimental Implementation , 2006, J. Intell. Transp. Syst..

[18]  R.E. Ritzmann,et al.  A crash avoidance system based upon the cockroach escape response circuit , 1997, Proceedings of International Conference on Robotics and Automation.

[19]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[20]  Mashrur Chowdhury,et al.  Real-Time Highway Traffic Condition Assessment Framework Using Vehicle–Infrastructure Integration (VII) With Artificial Intelligence (AI) , 2009, IEEE Transactions on Intelligent Transportation Systems.

[21]  Kenichi Yamada,et al.  Development of rear-end collision avoidance system , 1996, Proceedings of Conference on Intelligent Vehicles.

[22]  Daniel Krajzewicz,et al.  SUMO - Simulation of Urban MObility An Overview , 2011 .

[23]  Geert Wets,et al.  Understanding spatial concentrations of road accidents using frequent item sets. , 2005, Accident; analysis and prevention.

[24]  Yixuan Li,et al.  Motorway incident detection using probe vehicles , 2005 .

[25]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[26]  Henry Leung,et al.  Data fusion in intelligent transportation systems: Progress and challenges - A survey , 2011, Inf. Fusion.

[27]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[28]  L Mussone,et al.  An analysis of urban collisions using an artificial intelligence model. , 1999, Accident; analysis and prevention.

[29]  Adem Karahoca,et al.  Data Mining and Knowledge Discovery in Real Life Applications , 2009 .

[30]  Stephan Olariu,et al.  Intelligent Transportation Systems and Vehicular Networks that , 2022 .

[31]  Der-Horng Lee,et al.  Mobile Sensor and Sample-Based Algorithm for Freeway Incident Detection , 2002 .

[32]  Javier A. Barria,et al.  Detection and Classification of Traffic Anomalies Using Microscopic Traffic Variables , 2011, IEEE Transactions on Intelligent Transportation Systems.

[33]  Michael A. P. Taylor,et al.  The simple platoon advancement model of ITS technologies applied to vehicle control at signalised intersections , 2006 .

[34]  Stefan Krauss,et al.  MICROSCOPIC MODELING OF TRAFFIC FLOW: INVESTIGATION OF COLLISION FREE VEHICLE DYNAMICS. , 1998 .

[35]  Y. Ian Noy,et al.  Ergonomics and Safety of Intelligent Driver Interfaces , 1997 .

[36]  Hannes Hartenstein,et al.  A tutorial survey on vehicular ad hoc networks , 2008, IEEE Communications Magazine.

[37]  Ian H. Witten,et al.  WEKA: a machine learning workbench , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.

[38]  R L Cheu,et al.  FREEWAY INCIDENT DETECTION USING KINEMATIC DATA FROM PROBE VEHICLES , 2002 .

[39]  Jiuh-Biing Sheu.  A sequential detection approach to real-time freeway incident detection and characterization , 2004, Eur. J. Oper. Res..

[40]  Geert Wets,et al.  Traffic accident segmentation by means of latent class clustering. , 2008, Accident; analysis and prevention.

[41]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .