Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks

Abstract

Among discrete choice problems, travel mode choice modeling has received the most attention in the travel behavior literature. Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem in which multiple human behavioral patterns reflected by explanatory variables determine the choices between alternatives or classes. The capability and performance of two emerging pattern recognition data mining methods, decision trees (DT) and neural networks (NN), for work travel mode choice modeling were investigated. Models based on these two techniques are specified, estimated, and comparatively evaluated with a traditional multinomial logit (MNL) model. For comparison, a unique three-layer formulation of the MNL model is presented. The similarities and differences of the models' mechanisms and structures are identified, and the mechanisms and structures in the models' specifications and estimations are compared. Two performance measures, individual prediction rate and aggregate prediction rate, which represent the prediction accuracies for individual and mode aggregate levels, respectively, were applied to evaluate and compare the performances of the models. Diary data sets from the San Francisco, California, Bay Area Travel Survey 2000 were used for model estimation and evaluation. The prediction results show that the two data mining models offer comparable but slightly better performances than the MNL model in terms of the modeling results, while the DT model demonstrates the highest estimation efficiency and most explicit interpretability, and the NN model gives a superior prediction performance in most cases.

Using Decision Trees for Estimating Mode Choice of Trips in Buca-Izmir

“…Data mining is utilized for determining the factors of mode choice. Several studies used data mining tools in mode choice analysis so far ( Xie et al, 2003;Haluzova, 2008;Lu & Kawamura, 2010).…”

“…(Lu&Kawamura 2010, Yao, X. 2007, Xie et al 2003. In this study the factors influencing the mode choice by considering all types of trips and transport modes are determined with decision trees.…”

“…Although several studies used mode choice in modelling, but none of them used any process model. (Xie, C. et al, 2003;Lu & Kawamura, 2010) This paper describes how the process model can be used for identifying of factors affecting the mode choice.…”

Controle De Qualidade Posicional Em Dados Espaciais Utilizando Feições Lineares

“…In addition to the econometric techniques used to estimate travel behavior and mode choice, there are several studies that used data mining techniques to investigate the travel behavior. Mode choice modeling can be regarded as a pattern recognition problem in which multiple human behavioral patterns reflected by explanatory variables determine the choices between alternatives (Xie et al, 2007). Xie et al (2007) investigates the performance of two emerging pattern recognition data mining methods, decision trees (DT) analysis and neural networks (NN), for work travel mode choice modeling.…”

“…Mode choice modeling can be regarded as a pattern recognition problem in which multiple human behavioral patterns reflected by explanatory variables determine the choices between alternatives (Xie et al, 2007). Xie et al (2007) investigates the performance of two emerging pattern recognition data mining methods, decision trees (DT) analysis and neural networks (NN), for work travel mode choice modeling. Shmueli et al (1996) explored the application of neural networks to a behavioral transportation planning problem.…”