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This study creates an adaptive procedure for sequential forecasting of incident duration. This adaptive procedure includes two adaptive Artificial Neural Network-based models as well as the data fusion techniques to forecast incident duration. Model A is used to forecast the duration time at the time of incident notification, while Model B provides(More)
Exposure to pornography is routine among children and young people, with a range of notable and often troubling effects. Particularly among younger children, exposure to pornography may be disturbing or upsetting. Exposure to pornography helps to sustain young people's adherence to sexist and unhealthy notions of sex and relationships. And, especially among(More)
This paper develops two dynamic neural network structures to forecast short-term railway passenger demand. The first neural network structure follows the idea of autoregressive model in time series forecasting and forms a nonlinear autoregressive model. In addition, two experiments are tested to eliminate redundant inputs and training samples. The second(More)
This paper experiences a three-phrase back-propagation neural network approach to forecast short-term railway passenger demand. The first phase involves the selection of variables, the size of training data set, and the modification of stochastic outliers, under a specific origin-destination (O/D) pair of a given train service. In the second phase, in order(More)
To accommodate the heavy travel demand in high-density areas, Taipei Bus Station (TBS) is developed as the first multi-level bus terminal in Taipei City. TBS also plays important roles in congestion mitigation, energy conservation and pollutant reduction. Unlike conventional single-level terminals, bus flow interruption while circulating in TBS could(More)