Simulation Of Freeway Incident Detection Using Artificial Neural Networks

Abstract A major source of traffic delay in many large urban areas in the United States is non-recurring congestion caused by incidents. In the last several decades, a number of incident detection algorithms have been developed for freeway surveillance and control systems. However, conventional algorithms have generally met with mixed success in terms of performance criteria, such as detection rate, false alarm rate, and the mean time to detect incidents. The need for improved techniques is pressing, particularly with the advent of intelligent vehicle-highway system concepts. These systems will rely heavily on the ability to detect non-recurring traffic congestion automatically. In this paper, we hypothesize that spatial and temporal traffic patterns can be recognized and classified by an artificial neural network, and we present an investigation of such models for the automated detection of lane blocking incidents in a one-mile section of urban freeway. The artificial neural network was trained with data obtained from a microscopic freeway traffic simulation model that was specially calibrated for the actual freeway test section. The neural network first classifies the traffic state of the freeway section into either “incident-free” or “incident” conditions in every 30-second interval. The change in traffic state from incident-free to incident conditions is then used to trigger an incident alarm. Based on the results of an off-line test using simulated data, and comparisons with the well known California incident detection algorithm and the recently developed modified McMaster algorithm, the results suggest that neural network models have the potential to achieve significant improvements in incident-detection performance.

[1]  E R Case,et al.  DEVELOPMENT OF FREEWAY INCIDENT-DETECTION ALGORITHMS BY USING PATTERN-RECOGNITION TECHNIQUES , 1979 .

[2]  Stephen L Cohen,et al.  ANALYSIS OF FREEWAY RECONSTRUCTION ALTERNATIVES USING TRAFFIC SIMULATION , 1987 .

[3]  J A Lindley,et al.  URBAN FREEWAY CONGESTION: QUANTIFICATION OF THE PROBLEM AND EFFECTIVENESS OF POTENTIAL SOLUTIONS , 1987 .

[4]  D C Judycki,et al.  Managing traffic during nonrecurring congestion , 1992 .

[5]  Fred L. Hall,et al.  DISTINGUISHING BETWEEN INCIDENT CONGESTION AND RECURRENT CONGESTION: A PROPOSED LOGIC , 1989 .

[6]  Moshe Levin,et al.  INCIDENT DETECTION: A BAYESIAN APPROACH , 1978 .

[7]  G J Forbes.  IDENTIFYING INCIDENT CONGESTION , 1992 .

[8]  Stephen G. Ritchie,et al.  INVESTIGATION OF A NEURAL NETWORK MODEL FOR FREEWAY INCIDENT DETECTION , 1991 .

[9]  P K Houpt,et al.  Dynamic model-based techniques for the detection of incidents on freeways , 1980 .

[10]  Moshe Levin,et al.  Incident-detection algorithms. part 2. on-line evaluation , 1979 .

[11]  Stephen G. Ritchie,et al.  DEVELOPMENT OF AN INTELLIGENT SYSTEM FOR AUTOMATED PAVEMENT EVALUATION , 1991 .

[12]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[13]  G. Giuliano.  INCIDENT CHARACTERISTICS, FREQUENCY, AND DURATION ON A HIGH VOLUME URBAN FREEWAY , 1989 .

[14]  Lisa Aultman-Hall,et al.  A Catastrophe Theory Approach to Freeway Incident Detection , 1991 .

[15]  Samir A. Ahmed,et al.  APPLICATION OF TIME-SERIES ANALYSIS TECHNIQUES TO FREEWAY INCIDENT DETECTION , 1982 .

[16]  Alexander Skabardonis,et al.  APPLICATION OF SIMULATION TO EVALUATE THE OPERATION OF MAJOR FREEWAY WEAVING SECTIONS , 1989 .

[17]  Daniel B. Fambro,et al.  EVALUATION OF AN ALGORITHM FOR DETECTING URBAN FREEWAY INCIDENTS DURING LOW-VOLUME CONDITIONS , 1980 .

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