A Hybrid Approach for Automatic Incident Detection

This paper presents a hybrid approach to automatic incident detection (AID) in transportation systems. It combines time series analysis (TSA) and machine learning (ML) techniques in light of the fault diagnosis theory. In this approach, the time series component is to forecast the normal traffic for the current time point based on prior (normal) traffic. The ML component aims to detect incidents using features of real-time traffic, predicted normal traffic, as well as differences between the two. We validate our approach using a real-world data set collected in previous research. The results show that the hybrid approach is able to detect incidents more accurately [higher detection rate (DR)] and faster (shorter mean time to detect) under the requirement of a similar false alarm rate (FAR), as compared with state-of-the-art algorithms. This paper lends support to further studies on combining TSA with ML to address problems related to intelligent transportation systems (ITS).

[1]  Pravin Varaiya,et al.  The freeway service patrol evaluation project: Database support programs, and accessibility , 1996 .

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

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

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

[5]  Hussein Dia,et al.  Development and evaluation of neural network freeway incident detection models using field data , 1997 .

[6]  Wei Wang,et al.  Construct support vector machine ensemble to detect traffic incident , 2009, Expert Syst. Appl..

[7]  Bernhard Pfaff,et al.  VAR, SVAR and SVEC Models: Implementation Within R Package vars , 2008 .

[8]  Jay F. Nunamaker,et al.  Detecting Fake Websites: The Contribution of Statistical Learning Theory , 2010, MIS Q..

[9]  Hsun-Jung Cho,et al.  MISSING DATA TREATMENT AND DATA FUSION TOWARD TRAVEL TIME ESTIMATION FOR ATIS , 2005 .

[10]  A. R. Cook,et al.  DETECTION OF FREEWAY CAPACITY-REDUCING INCIDENTS BY TRAFFIC-STREAM MEASUREMENTS , 1974 .

[11]  Zhaosheng Yang,et al.  A new algorithm of incident detection on freeways , 2001, IVEC2001. Proceedings of the IEEE International Vehicle Electronics Conference 2001. IVEC 2001 (Cat. No.01EX522).

[12]  Henry S.L. Fan,et al.  Evaluation of the Transferability of Incident Detection Algorithms Developed for Singapore Expressways , 2005 .

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  Clarissa Han,et al.  Freeway Incident Detection Technologies and Techniques , 2010 .

[15]  Shuming Tang,et al.  A survey of vision-based automatic incident detection technology , 2005, IEEE International Conference on Vehicular Electronics and Safety, 2005..

[16]  Haitham Al-Deek,et al.  Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models , 2009, J. Intell. Transp. Syst..

[17]  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.

[18]  Stephen G. Ritchie,et al.  CALIBRATION OF INTRAS FOR SIMULATION OF 30-SEC LOOP DETECTOR OUTPUT , 1994 .

[19]  Hojjat Adeli,et al.  An Adaptive Conjugate Gradient Neural Network–Wavelet Model for Traffic Incident Detection , 2000 .

[20]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[21]  Stephen G. Ritchie,et al.  STATISTICAL AND NEURAL CLASSIFIERS TO DETECT TRAFFIC OPERATIONAL PROBLEMS ON URBAN ARTERIALS , 1998 .

[22]  Yorgos J. Stephanedes,et al.  FREEWAY INCIDENT DETECTION THROUGH FILTERING , 1993 .

[23]  Hani S. Mahmassani,et al.  Evaluation of Incident Detection Methodologies , 1999 .

[24]  Dipti Srinivasan,et al.  Development of an intelligent technique for traffic network incident detection , 2000 .

[25]  Yi Qi,et al.  Detection-delay-based freeway incident detection algorithms , 2003 .

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

[27]  Kun Zhang,et al.  Effective arterial road incident detection: A Bayesian network based algorithm , 2006 .

[28]  Kun Zhang,et al.  Towards Universal Freeway Incident Detection Algorithms , 2006 .

[29]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[30]  Shuming Tang,et al.  Traffic-incident detection-algorithm based on nonparametric regression , 2005, IEEE Transactions on Intelligent Transportation Systems.

[31]  H. T. Kung.  Big Data and Compressive Sensing , 2012, IC3.

[32]  Milos Hauskrecht,et al.  Learning to detect incidents from noisily labeled data , 2009, Machine Learning.

[33]  GERNOT METZE,et al.  On the Connection Assignment Problem of Diagnosable Systems , 1967, IEEE Trans. Electron. Comput..

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

[35]  Ruey Long Cheu,et al.  Automated detection of lane-blocking freeway incidents using artificial neural networks , 1995 .

[36]  Alex M. Andrew,et al.  Fault Diagnosis: Models, Artificial Intelligence, Applications , 2005 .

[37]  Jun Cai,et al.  Video-Based Automatic Incident Detection for Smart Roads: The Outdoor Environmental Challenges Regarding False Alarms , 2008, IEEE Transactions on Intelligent Transportation Systems.

[38]  Emily Parkany,et al.  A COMPLETE REVIEW OF INCIDENT DETECTION ALGORITHMS & THEIR DEPLOYMENT: WHAT WORKS AND WHAT DOESN'T , 2005 .

[39]  D. J. Fonseca,et al.  The Role of Non-Recurring Congestion in Massive Hurricane Evacuation Events , 2011 .

[40]  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.

[41]  Henry S. L. Fan,et al.  Development of Dual-Station Automated Expressway Incident Detection Algorithms , 2007, IEEE Transactions on Intelligent Transportation Systems.

[42]  Edward Chung,et al.  Characterization of incidents on an urban arterial road , 2001 .

[43]  Kevin N. Balke,et al.  An Investigation into the Evaluation and Optimization of the Automatic Incident Detection Algorithm Used in TxDOT Traffic Management Systems , 2005 .

[44]  Fei-Yue Wang,et al.  Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications , 2010, IEEE Transactions on Intelligent Transportation Systems.

[45]  Wei Wang,et al.  Incident detection algorithm based on partial least squares regression , 2008 .

[46]  Kar-Ann Toh,et al.  Reduced multivariate polynomial-based neural network for automated traffic incident detection , 2008, Neural Networks.

[47]  Billy M. Williams,et al.  Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models , 1998 .

[48]  N. E. Thomas,et al.  Multi-state and multi-sensor incident detection systems for arterial streets , 1998 .