Training Neural Networks to Detect Freeway Incidents by Using Particle Swarm Optimization

Among the many models and techniques developed to automatically detect freeway incidents in recent years, the multilayer feed-forward neural network (MLF) is one of the most promising models in terms of high detection rate, low false alarm rate, and faster mean time to detection. The use of particle swarm optimization (PSO) algorithms to train MLFs to detect freeway incidents is investigated in an attempt to further improve detection performance. Several MLFs have been trained by different variations of the PSO methods using real incident data from Interstate 880 in California. The best MLFs were compared with one trained by the conventional backpropagation (BP) algorithm. The evaluation discussed, based on I-880 data, shows that the MLFs trained by the PSO algorithms have the same or higher detection rates, similar or lower false alarm rates, and faster mean time to detection than the MLF trained by the BP algorithm. This research has shown that PSO has the potential to improve the good incident detection performance of the MLF.

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

[2]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[3]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  S G Ritchie,et al.  Neural network model for automated detection of lane-blocking freeway incidents , 1994 .

[5]  G. Hyman.  Trip distribution and modal split by categories of households , 1970 .

[6]  Gordon F. Newell,et al.  Optimal spacings for a rectangular grid transportation network—II: Revised assignments , 1976 .

[7]  Hojjat Adeli,et al.  FUZZY-WAVELET RBFNN MODEL FOR FREEWAY INCIDENT DETECTION , 2000 .

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

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

[10]  Michael J. Cassidy,et al.  Application of Fuzzy Logic and Neural Networks to Automatically Detect Freeway Traffic Incidents , 1994 .

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

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

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