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A freeway incident detection algorithm is developed using back propagation neural networks. Based on real-time occupancy and volume counts from pairs of adjacent loop detector stations, the network is trained with actual data, including 31 incidents from a typical freeway in the Twin Cities Metropolitan Area over the afternoon peak period during 72 days.