Size Matters: The Use and Misuse of Statistical Significance in Discrete Choice Models in the Transportation Academic Literature

In this paper we review the academic transportation literature published between 2014 and 2018 to evaluate where the field stands regarding the use and misuse of statistical significance in empirical analysis, with a focus on discrete choice models. Our results show that 39% of studies explained model results exclusively based on the sign of the coefficient, 67% of studies did not distinguish statistical significance from economic, policy or scientific significance in their conclusions, and none of the reviewed studies considered the statistical power of the tests. Based on these results we put forth a set of recommendations aimed at shifting the focus away from statistical significance towards proper and comprehensive assessment of effect magnitudes and other policy relevant quantities.Comment: 14 pages, 1 table, 0 figure

Text-aided Group Decision-making Process Observation Method (x-GDP): A novel methodology for observing the joint decision-making process of travel choices

Joint travel decisions, particularly related to social activities remain poorly explained in traditional behavioral models. A key reason for this is the lack of empirical data, and the difficulties associated with collecting such data in the first place. To address this problem, we propose Text-aided Group Decision-making Process Observation Method (x-GDP), a novel survey methodology to collect data on joint leisure activities, from all members of a given clique. Through this method we are able to observe not only the outcome (i.e., the joint activity location chosen) but also the decision-making process itself, including the alternatives that compose the choice set, individual and clique characteristics that might affect the choice process, as well as the discussion behind the choice via texts. Observing such a process will allow researchers to gain a deeper understanding of the joint decision-making process, including how alternatives are weighted, how members interact with each other, and finally how joint choices are made. In this paper we introduce the results of a x-GDP survey implementation focusing on joint eating-out activities in the Greater Tokyo Area, giving a detailed overview of the survey components, execution logistics and initial insights on the data. This is to the best of our knowledge the first attempt to observe group joint travel decisions in real time through a zoom-moderated experiment.Comment: 19 pages, 8 figure

都市の物的環境と交通行動の因果関係に関する研究 : 日本の諸都市を事例として

学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 原田 昇, 東京大学教授 浅見 泰司, 東京大学准教授 大森 宣暁, 東京大学教授 羽藤 英二, 東京大学教授 加藤 浩徳University of Tokyo(東京大学

On the effect of the built environment and preferences on non-work travel: Evidence from Japan

This study uses data from the 4th Nationwide Person Trip Survey to analyse the relation between the built environment, modal access preference at residential location and travel behaviour in Japan. By estimating random parameter count models, significant statistical associations were found between the built environment and preferences with non-work trip frequency by mode. Furthermore the effect of population density, car ownership and some access preference traits were found to be heterogeneous for some modes. Since most of the recent literature has focused largely on North-American and European cities, this study contributes to the existing body of literature by examining the role of the built environment and individual preferences on travel behaviour in the context of Japanese cities, and sheds some light on existing heterogeneity in the effects of some factors related to travel behaviour

Size matters: The use and misuse of statistical significance in discrete choice models in the transportation academic literature

In this paper we review the academic transportation literature published between 2014 and 2018 to evaluate where the field stands regarding the use and misuse of statistical significance in empirical analysis, with a focus on discrete choice models. Our results show that 39% of studies explained model results exclusively based on the sign of the coefficient, 67% of studies did not distinguish statistical significance from economic, policy or scientific significance in their conclusions, and none of the reviewed studies considered the statistical power of the tests. Based on these results we put forth a set of recommendations aimed at shifting the focus away from statistical significance towards proper and comprehensive assessment of effect magnitudes and other policy relevant quantities.ISSN:0049-4488ISSN:1572-943

Size matters: The use and misuse of statistical significance in discrete choice models in the transportation academic literature

In this paper we review the academic transportation literature published between 2014 and 2018 to evaluate where the field stands regarding the use and misuse of statistical significance in empirical analysis, with a focus on discrete choice models. Our results show that 39% of studies explained model results exclusively based on the sign of the coefficient, 67% of studies did not distinguish statistical significance from economic, policy or scientific significance in their conclusions, and none of the reviewed studies considered the statistical power of the tests. Based on these results we put forth a set of recommendations aimed at shifting the focus away from statistical significance towards proper and comprehensive assessment of effect magnitudes and other policy relevant quantities

Built environment and travel behavior: Validation and application of a continuous-treatment propensity score stratification method

This article discusses the validation and implementation of a propensity score approach with continuous treatment to test the existence of a causal relationship between the built environment and travel behavior using cross-sectional data. The implemented methodology differs from previous applications in the planning literature in that it relaxes the binary treatment assumption, which polarizes the built environment into two extremes (e.g., urban vs suburban). The effectiveness of the proposed methodology in reducing bias was validated via Monte Carlo simulation. The proposed approach was shown to reduce self-selection bias against Ordinary Least Squares (OLS) regression in all but extreme levels of non-linearity. Empirical results suggest that an increase in urbanization has a negative effect on home-based maintenance car trip frequencies, and conversely, a positive effect on home-based maintenance non-motorized trip frequencies. Result estimates suggest the existence of a causal mode substitution mechanism between car and non-motorized modes given increases in the urbanization level at residential locations, thus providing some empirical support to the arguments put forth by compact city advocates

Hetereogenous travel activity patterns in Japan: Accounting for inter-dependencies in mobility tool use

Using data from the Japanese 5 th nationwide person trip survey, a joint model of trip frequencies by mode (car, transit and non-motorized) is estimated using a generalized heterogeneous data model to account for inter-dependencies stemming from unobserved factors. The estimated model successfully captures the effect of common unobserved factors on trip frequencies by mode underscoring the need to account for modal inter-dependencies to avoid bias in parameter estimates. Consistent with findings from the literature, results suggest the existence of substantial mode substitution effects between car and transit and non-motorized modes given changes in accessibility levels. Furthermore, given common unobserved factors, statistically significant and substantial substitution effects were observed among all modes