Drivers’ Route Choice Behavior: Analysis by Data Mining Algorithms

Abstract

Decision trees and production rules, which are among the methods used in knowledge discovery and data mining, are applied to investigate drivers’ route choice behavior. These methods have an advantage over artificial neural networks, another data mining method often used in analysis of travel behavior: they facilitate determination of the relationships between the explanatory variables and the choice. Specifically, the C4.5 algorithm, which produces a decision tree and a set of production rules from the tree, is applied here. Two surveys were carried out to collect data on drivers’ route choice behavior between two alternative routes on expressway networks. The two data sets include the expected minimum, maximum, and average travel times along each alternative route, as indicated by the respondent as well as his or her sociodemographic attributes. The results of the analyses suggest that different expected travel times influence route choice in different cases and that a maximum or average travel time determines route choice in some cases regardless of other attributes. The results of a comparison analysis between the C4.5 algorithm and discrete choice models indicate the superior ability offered by the former in representing drivers’ route choice.

Incorporating Household Gathering and Mode Decisions in Large‐Scale No‐Notice Evacuation Modeling

“…In this study, a decision tree (DT) approach models mode choice for evacuation situations. DT is a classification method which constructs a tree with a set of rules; DT has been recently applied to discrete choice behavior analysis (e.g., travel mode choice) and achieved considerable benefits for prediction performance (Thill and Wheeler, ; Wets et al, ; Yamamoto et al, ; Xie et al, ).…”

“…DT has been recently applied to discrete choice behavior analysis (e.g., travel mode choice) and achieved considerable benefits for prediction performance (Thill and Wheeler, 2000;Wets et al, 2000;Yamamoto et al, 2002;Xie et al, 2003). The Classification and Regression Tree (CART) algorithm, a binary nonparametric recursive-partitioning classification tool introduced by Breiman et al (1984), is used in this study.…”

The Impact of Beijing Subway’s New Fare Policy on Riders’ Attitude, Travel Pattern and Demand

“…In recent years, the tree-based algorithm has been gradually used in the analysis of transportation studies for analyzing the traffic accident rates and injury severity problems [43][44][45], investigating the travel behavior and transport mode choice [46,47] and identifying the interaction of different transportation characteristics [48].…”

Understanding drivers' route choice behaviours in the urban network with machine learning models

“…In these papers, decision trees, RF, SVM, and neural network (NNET) algorithms are used and compared. As for the route choice analysis, the first paper that applied the ML method should be the work from Yamamoto et al [22]. They analysed the route choice behaviours on Japanese highways with a decision tree.…”