Estimation of trip attraction and analyzing its main influencing factors are powerful for offering different classifications for business districts and presenting recommendations for improving attractiveness in long term. This is beneficial for designing transportation facilities and infrastructures. The paper presents the prediction of trip attraction using an artificial intelligence technology due to the profits that the technology can possess in shortening time, lowering expenses and saving effort. The new model has utilized six input parameters that have not been considered previously within the area of Nasiriyah city including; age and educational level of the passengers, mode of transport that the passengers use, purpose of the trip, frequency of the weekly visit, and the distance towards the central business district. In this study, the independences - trip attraction data of 224 sets are collected through field observations and home interviews within the area. Neural Network Toolbox in MATLAB is utilized, which is dealt with the six key independences as input whereas with the trip attraction as the output desired to be expected. The model has been generated by adoption of twenty-five artificial neurons in only one single hidden layer. The outcomes have showed a good performance in predicting the trip attraction by utilizing artificial neural network. The coefficient of correlation for training is 0.81445 and for all, including training, testing, and validation, it is 0.73825. The study produces a reliable model as an alternative to complex, high-priced and/or time-consuming models.
