01697464

Component

To explain and predict active school travel (AST), most studies rely on some types of analysis which do not reflect unobserved taste heterogeneity of various factors at the individual level. The present study aims: (1) to identify any heterogeneity or random taste variation among decision makers, and speculate on its possible source; and (2) to evaluate whether a more sophisticated model (i.e. a model that considers taste heterogeneity) such as mixed logit model (MXL) including random parameter (RP) and random parameter analysis (RPA) materially improves the accuracy of AST prediction compared to a simpler model; the multinomial logit (MNL) model. The database comprised 735 valid observations (from 1078 randomly distributed questionnaires in a school travel survey that had been conducted in 2014). The results show that the aggregate direct elasticity of choosing AST, with respect to the perceived walking time to school (PWTS), is -0.78% in the MNL model, -0.84% in the RP model, and -0.85% in the RPA model. With 10% increase of the PWTS the MNL model would say that the AST share would decrease by 7.8% (from 18.9% to 17.4%) while the RPA model would say that it decreases by 8.5% (from 18.9% to 17.3%), that essentially indistinguishable. Thus, the expected share of AST is overestimated by MNL by one-tenth of a percentage point. The findings show that considering taste heterogeneity does not necessarily improve the accuracy of analysis for the aggregate share of the AST respect to policy-sensitive variables in the current database.

This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.

19-04414

English

15p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References (31) ; Tables

Attitudes; Mathematical models; Mathematical prediction; Nonmotorized transportation; School trips; Travel behavior; Travel time; Walking distance

Pedestrians and Bicyclists; Safety and Human Factors

Transportation Research Board Annual Meeting 2019 Paper #19-04414

TRIS, TRB, ATRI

Dec 7 2018 9:28AM