Classification of Freeway Traffic Patterns for Incident Detection Using Constructive Probabilistic Neural Networks  

Oleh:

Srinivasan, D. ; Jin, Xin ; Ruey, Long Cheu

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 5 (2001), page 1173-1187.
Topik: probabilistic thinking; freeway; traffic patterns; incident; detection; probabilistic; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper proposes a new technique for freeway incident detection using a constructive probabilistic neural network (CPNN). The CPNN incorporates a clustering technique with an automated training process. The work reported in this paper was conducted on Ayer Rajah Expressway (AYE) in Singapore for incident detection model development, and subsequently on I - 880 freeway in California, for model adaptation. The model developed achieved incident detection performance of 92 % detection rate and 0.81 % false alarm rate on AYE, and 91.30 % detection rate and 0.27 % false alarm rate on I -880 freeway using the proposed adaptation method. In addition to its superior performance, the network pruning method employed facilitated model size reduction by a factor of 11 compared to a conventional probabilistic neural network. A more impressive size reduction by a factor of 50 was achieved after the model was adapted for the new site. The results from this paper suggest that CPNN is a better adaptive classifier for incident detection problem with a changing site traffic environment.

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