Artificial Neural Network-Based Approach To Modeling Trip Production

Abstract

Accurate and reliable estimates of trip production of a study area are important for an accurate forecast from the four-step travel demand forecasting procedure. In the trip generation step, trip production estimates are considered more accurate, and trip attractions are adjusted while keeping the productions constant. This means that more accurate trip production rates will result in more reliable forecasts. Improving the accuracy of forecasts requires an extensive and reliable data base or improvement in the modeling techniques. Since data base enhancement is costly and time-consuming, an alternative methodology is proposed and examined for trip production prediction using artificial neural network (ANN) concepts and techniques. The data base used was made available by the Delaware Department of Transportation. The data were collected for 60 sites throughout Delaware between 1970 and 1974 and are based on field counts and home interviews. Twenty-six regression models were calibrated on these data. In addition, 18 ANN architectures were developed, and their predictions were compared with those from regression models. Comparisons indicate that the ANNs have the capability to represent the relationship between the trip production rate and the independent variables more accurately than regression analysis at no additional cost of increasing the data base.

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