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Public administration has adopted information and communication technology in order to construct new intelligent systems and design new risk prevention strategies in transportation management. The ultimate goal is to improve the quality of the transportation services and also to ensure public transportation safety. In this research, a combination of spatial clustering methods and artificial neural network models was used in order to predict the high crime risk transportation areas. Geographic information systems were used to perform spatial analysis so as to identify the regions with a high concentration of crime incidents. Artificial intelligence was used in this study in order to build artificial neural network predictive models. The neural network predictive models were evaluated by using the Mean Squared Error (MSE) in order to find the optimal forecasting model. The optimal forecasting model was used in order to predict the high crime risk transportation areas. The scaled conjugate gradient algorithm was utilized as the training algorithm for the construction of the feedforward neural network models, since it is considered as one of the fastest learning algorithms compared to several other algorithms such as backpropagation learning algorithms.

The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment