Abstract
Logistic regression models have been widely used in previous studies to analyze public transport utilization. These studies have shown travel time to be an indispensable variable for such analysis and usually consider it to be a deterministic variable. This formulation does not allow us to capture travelers’ perception error regarding travel time, and recent studies have indicated that this error can have a significant effect on modal choice behavior. In this study, we propose a logistic regression model with a hierarchical random error term. The proposed model adds a new random error term for the travel time variable. This term structure enables us to investigate travelers’ perception error regarding travel time from a given choice behavior dataset. We also propose an extended model that allows constraining the sign of this error in the model. We develop two Gibbs samplers to estimate the basic hierarchical model and the extended model. The performance of the proposed models is examined using a well-known dataset.
Highlights
Understanding the utilization of public transport is important for policy design and urban traffic planning
McGillivray [1] proposed the binary choice problem in the two-modal case and applied a logistic model to investigate the dependence of modal choice on the individual values of time and cost by modal
This study proposes a logistic regression model with a hierarchical random error term to analyze the binary choice problem
Summary
Understanding the utilization of public transport is important for policy design and urban traffic planning. From a behavior analysis perspective, such utilization can be analyzed in terms of a binary choice problem in which the traveler must choose between public transit and a private mode of transport. Previous studies usually employ logistic regression models to discuss this binary choice problem. These models can be used to predict choice probability and to evaluate the effect of various attitudes on the utilization of public transport. We adopt hierarchical error terms to solve the problem We add these terms after important attributes such as travel time rather than after the entire utility function in order to capture travelers’ perception error regarding the attributes. We evaluate the performance of the proposed models using a well-known dataset provided by Horowitz [13, 14]
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