Multidimensional Data Mining of Traffic Anomalies on Large-Scale Road Networks

The identification and characterization of traffic anomalies on massive road networks is a vital component of traffic monitoring and control. Anomaly identification can be used to reduce congestion, increase safety, and provide transportation engineers with better information for traffic forecasting and road network design. However, because of the size, complexity, and dynamics of transportation networks, automating such a process is challenging. A multidimensional mining framework is proposed; it can be used to identify a concise set of anomalies from massive traffic monitoring data and then overlay, contrast, and explore such anomalies in multidimensional space. The framework is based on the development of two novel methods: (1) efficient anomaly mining stemming from the discovery of the atypical fragment (a compact representation of a set of abnormal traffic patterns happening across a sequence of connected road segments, possibly spanning multiple roads, and occurring at overlapping time intervals) and (2) a multidimensional anomaly overlay model that enables the clustering of multiple atypical fragments according to different criteria (e.g., severity, topology, or spatiotemporal characteristics). The atypical fragment provides a concise, global view of the traffic anomaly situation, whereas the framework for anomaly overlay provides the power of online analytical processing to facilitate the discovery of patterns associated with different anomaly types and the navigation of anomalies at multilevel abstraction.

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

[2]  Adolf D May,et al.  FREEWAY DETECTOR DATA ANALYSIS : SMART CORRIDOR SIMULATION EVALUATION , 1993 .

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

[4]  Symeon Papavassiliou,et al.  Enhancing network traffic prediction and anomaly detection via statistical network traffic separation and combination strategies , 2006, Comput. Commun..

[5]  Alexander Skabardonis,et al.  Detecting Errors and Imputing Missing Data for Single-Loop Surveillance Systems , 2003 .

[6]  Karl Petty,et al.  INCIDENTS ON THE FREEWAY : DETECTION AND MANAGEMENT , 1997 .

[7]  Jiawei Han,et al.  Adaptive Fastest Path Computation on a Road Network: A Traffic Mining Approach , 2007, VLDB.

[8]  Dipti Srinivasan,et al.  Support vector machine models for freeway incident detection , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[9]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.

[10]  Eric Horvitz,et al.  Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service , 2005, UAI.

[11]  Harold J. Paynet.  Freeway Incident-Detection Algorithms Based on Decision Trees With States , 2022 .

[12]  E.C.-P. Chang,et al.  Fuzzy systems based automatic freeway incident detection , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[13]  Angshuman Guin,et al.  An Incident Detection Algorithm Based On a Discrete State Propagation Model of Traffic Flow , 2004 .

[14]  Hans-Peter Kriegel,et al.  Incremental Clustering for Mining in a Data Warehousing Environment , 1998, VLDB.

[15]  Margo J. Anderson,et al.  The Census , 1921, The Hospital.

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

[17]  Dipti Srinivasan,et al.  DEVELOPMENT AND ADAPTATION OF CONSTRUCTIVE PROBABILISTIC NEURAL NETWORK IN FREEWAY INCIDENT DETECTION , 2002 .

[18]  P Spaulding,et al.  NATIONAL TRANSPORTATION STATISTICS , 1983 .

[19]  Billy M. Williams,et al.  Traffic Management Center Use of Incident Detection Algorithms: Findings of a Nationwide Survey , 2007, IEEE Transactions on Intelligent Transportation Systems.

[20]  RamakrishnanRaghu,et al.  Bottom-up computation of sparse and Iceberg CUBE , 1999 .

[21]  Arnold P. Boedihardjo,et al.  AOID: adaptive on-line incident detection system , 2006, 2006 IEEE Intelligent Transportation Systems Conference.