Assessing the Relative Importance of Input Variables for Route Choice Modeling: A Neural Network Approach

抄録

This paper identifies the relative importance of variables influencing route choice using a neural network approach. Variables related to route attributes and choice contexts are simultaneously incorporated into the model, and a weight partition algorithm is employed to calculate the strength of influence on route choice decisions. The network is trained and validated using stated preference data. Simulation results show good predictability (97.4% of accuracy) of the neural network model. The relative importance of input variables indicates that road category, pricing, bonus and passing through an urban area are more important. Among all choice contexts, the size of truck is most important, followed by travel time difference and road length. The relative importance identified by the neural network model is consistent with the results of a multinomial logit model, and provide meaningful references for variable selection and model estimation.