This study analyses empirically how built environment affects school travel with a focus on independence from adults and travel mode. Students in three elementary schools—Chinan, Jingmei and Xinhwa—in Taipei’s Wenshan District are analysed after questionnaire surveys. The survey data are analysed using nested logit models at two decision levels. Analytical results indicate that high shade-tree density and high sidewalk coverage encourage children to walk to school independently, while large block sizes and increased intersection numbers discourage children from walking to school independently. Furthermore, although high building density, high vehicle density and diversified mode option encourage children to travel home after school by walking, bus or vanpool, block size and road width discourage children from so doing. These results are mostly similar to the findings of previous studies, although they also have some differences. Based on the empirical evidence presented in this study, three strategies are recommended for reshaping the built environment in Taipei: compact structure, pedestrian-friendly design and frequent bus services.

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We present an efficient approach to forming feature maps. The method involves three stages. In the first stage, we use the K-means algorithm to select N2 (i.e., the size of the feature map to be formed) cluster centers from a data set. Then a heuristic assignment strategy is employed to organize the N2 selected data points into an N x N neural array so as to form an initial feature map. If the initial map is not good enough, then it will be fine-tuned by the traditional Kohonen self-organizing feature map (SOM) algorithm under a fast cooling regime in the third stage. By our three-stage method, a topologically ordered feature map would be formed very quickly instead of requiring a huge amount of iterations to fine-tune the weights toward the density distribution of the data points, which usually happened in the conventional SOM algorithm. Three data sets are utilized to illustrate the proposed method.

In this paper a new model of self-organizing neural networks is proposed. An algorithm called "double self-organizing feature map" (DSOM) algorithm is developed to train the novel model. By the DSOM algorithm the network will adaptively adjust its network structure during the learning phase so as to make neurons responding to similar stimulus have similar weight vectors and spatially move nearer to each other at the same time. The final network structure allows us to visualize high-dimensional data as a two dimensional scatter plot. The resulting representations allow a straightforward analysis of the inherent structure of clusters within the input data. One high-dimensional data set is used to test the effectiveness of the proposed neural networks.

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