Estimation of percentage of pass‐by trips generated by a shopping center using artificial neural networks

Abstract

Pass‐by trips are trips made as intermediate stops on the way from an origin to a primary trip destination. Accurate estimates of the percentage of pass‐by trips generated by a land use are extremely important for both planners and developers. The traditional method of pass‐by trip estimation is regression modeling with the help of the U.S. Institute of Transportation Engineers (ITE) Trip Generation manual. This paper also uses data from the Trip Generation manual, and focuses on an alternative methodology based on Artificial Neural Networks (ANNs). Use is made of backpropogation, a popular ANN paradigm, and five different architectures of backpropogations are developed, tested and compared against three different regression models — linear, log‐log and log‐linear forms, respectively. The results from the regression and ANN‐based models are compared in terms of the Root Mean Square of Errors (RMSE) of predicted values. It is found that the worst ANN prediction RMSE is lower than the best regression prediction RMSE. ANN‐based models have the capability of representing the relationship between the percentage of pass‐by trips and the independent variables more accurately than regression analysis at no additional monetary costs.

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