Development and evaluation of arterial incident detection models using fusion of simulated probe vehicle and loop detector data

This paper describes the development of neural network models for automatic incident detection on arterial roads, using simulated data derived from inductive loop detectors and probe vehicles. The work reported in this paper extends previous research by comparing the performance of various data fusion neural network architectures and assessing model performance for various probe vehicle penetration rates and loop detector configurations. Data from 108 incidents was collected from loop detectors and probe vehicles using a calibrated and validated traffic simulation model. The best performance was obtained for detector configurations found on most existing road networks, with a detection rate of 86%, false alarm rate of 0.36% and probe vehicle penetration rate of 20%. Fusion of speed data further improved performance, resulting in an incident detection rate of 90% and a false alarm rate of 0.5%. The results reported in this paper demonstrate the feasibility of developing advanced data fusion neural network architectures for detection of incidents on urban arterials using data from existing loop detector configurations and probe vehicles.

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

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

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

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

[5]  Frank S. Koppelman,et al.  USE OF VEHICLE POSITIONING DATA FOR ARTERIAL INCIDENT DETECTION , 1996 .

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

[7]  Mgh Bell,et al.  AUTOMATIC DETECTION OF TRAFFIC INCIDENTS ON A SIGNAL-CONTROLLED ROAD NETWORK , 1988 .

[8]  Yi Qi,et al.  Application of wavelet technique to freeway incident detection , 2003 .

[9]  Hussein Dia,et al.  Assessment of incident-induced impacts on the performance of an arterial network , 2001 .

[10]  Yaser E. Hawas.  A fuzzy-based system for incident detection in urban street networks , 2007 .

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

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

[13]  Fedel Frank Saccomanno,et al.  Analysis of Freeway Accident Detection , 1997 .

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

[15]  Yorgos J. Stephanedes,et al.  SMOOTHING ALGORITHMS FOR INCIDENT DETECTION , 1993 .

[16]  Hussein Dia,et al.  Simulation of arterial incident detection using neural networks , 2001 .

[17]  Stephen G. Ritchie,et al.  Simulation Of Freeway Incident Detection Using Artificial Neural Networks , 1993 .

[18]  Lee D. Han,et al.  AUTOMATIC DETECTION OF TRAFFIC OPERATIONAL PROBLEMS ON URBAN ARTERIALS , 1989 .

[19]  Hussein Dia,et al.  Neural network incident detection on arterials using fusion of simulated probe vehicle and loop detector data , 2005 .

[20]  Joseph L. Schofer,et al.  Arterial incident detection using fixed detector and probe vehicle data , 1995 .

[21]  K. Thomas,et al.  Development and evaluation of fractal dimension models for freeway incident detection , 2004 .

[22]  John N. Ivan,et al.  REAL-TIME DATA FUSION FOR ARTERIAL STREET INCIDENT DETECTION USING NEURAL NETWORKS , 1995 .

[23]  Fred L. Hall,et al.  Catastrophe theory and patterns in 30 second freeway traffic data, implications for incident detection , 1989 .

[24]  Bhagwant Persaud,et al.  CONGESTION IDENTIFICATION ASPECTS OF THE MCMASTER INCIDENT DETECTION ALGORITHM , 1990 .