Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. What is less well understood is the extent to which the observed patterns of travel behavior can be attributed to the residential built environment itself, as opposed to the prior self-selection of residents into a built environment that is consistent with their predispositions toward certain travel modes and land use configurations. To date, most studies addressing this attitudinal self-selection issue fall into seven categories: direct questioning, statistical control, instrumental variables models, sample selection models, joint discrete choice models, structural equations models, and longitudinal designs. This paper reviews and evaluates these alternative approaches with respect to this particular application (a companion paper focuses on the empirical findings of 28 studies using these approaches). We identify some advantages and disadvantages of each approach, and note the difficulties in actually quantifying the absolute and/or relative extent of the true influence of the built environment on travel behavior. Although time and resource limitations are recognized, we recommend usage of longitudinal structural equations modeling with control groups, a design which is strong with respect to all causality requisites.

/pdf/examining-the-impacts-of-residential-self-selection-on-16l24vursx.pdf

Examining the impacts of residential self-selection on travel behavior: A focus on methodologies

Several consumer demand choices are characterized by the choice of multiple alternatives simultaneously. An example of such a choice situation in activity-travel analysis is the type of discretionary (or leisure) activity to participate in and the duration of time investment of the participation. In this context, within a given temporal period (say a day or a week), an individual may decide to participate in multiple types of activities (for example, in-home social activities, out-of-home social activities, in-home recreational activities, out-of-home recreational activities, and out-of-home non-maintenance shopping activities). In this paper, we derive and formulate a utility theory-based model for discrete/continuous choice that assumes diminishing marginal utility as the level of consumption of any particular alternative increases (i.e., satiation). This assumption yields a multiple discreteness model (i.e., choice of multiple alternatives can occur simultaneously). This is in contrast to the standard discrete choice model that is based on assuming the absence of any diminishing marginal utility as the level of consumption of any alternative increases (i.e., no satiation), leading to the case of strictly single discreteness. The econometric model formulated here, which we refer to as the multiple discrete–continuous extreme value (MDCEV) model, has a surprisingly simple and elegant closed form expression for the discrete–continuous probability of not consuming certain alternatives and consuming given levels of the remaining alternatives. To our knowledge, we are the first to develop such a simple and powerful closed-form model for multiple discreteness in the literature. This formulation should constitute an important milestone in the area of multiple discreteness, just as the multinomial logit (MNL) represented an important milestone in the area of single discreteness. Further, the MDCEV model formulated here has the appealing property that it collapses to the familiar multinomial logit (MNL) choice model in the case of single discreteness. Finally, heteroscedasticity and/or correlation in unobserved characteristics affecting the demand of different alternatives can be easily incorporated within the MDCEV model framework using a mixing approach. The MDCEV model and its mixed variant are applied to analyze time-use allocation decisions among a variety of discretionary activities on weekends using data from the 2000 San Francisco Bay Area survey.

/pdf/a-multiple-discrete-continuous-extreme-value-model-3blqcmlr2c.pdf

A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions

The integrated modeling of land use and transportation choices involves analyzing a continuum of choices that characterize people’s lifestyles across temporal scales. This includes long-term choices such as residential and work location choices that affect land-use, medium-term choices such as vehicle ownership, and short-term choices such as travel mode choice that affect travel demand. Prior research in this area has been limited by the complexities associated with the development of integrated model systems that combine the long-, medium- and short-term choices into a unified analytical framework. This paper presents an integrated simultaneous multi-dimensional choice model of residential location, auto ownership, bicycle ownership, and commute tour mode choices using a mixed multidimensional choice modeling methodology. Model estimation results using the San Francisco Bay Area highlight a series of interdependencies among the multi-dimensional choice processes. The interdependencies include: (1) self-selection effects due to observed and unobserved factors, where households locate based on lifestyle and mobility preferences, (2) endogeneity effects, where any one choice dimension is not exogenous to another, but is endogenous to the system as a whole, (3) correlated error structures, where common unobserved factors significantly and simultaneously impact multiple choice dimensions, and (4) unobserved heterogeneity, where decision-makers show significant variation in sensitivity to explanatory variables due to unobserved factors. From a policy standpoint, to be able to forecast the “true” causal influence of activity-travel environment changes on residential location, auto/bicycle ownership, and commute mode choices, it is necessary to capture the above-identified interdependencies by jointly modeling the multiple choice dimensions in an integrated framework.

/pdf/modeling-the-choice-continuum-an-integrated-model-of-nxtbsochuw.pdf

Modeling the choice continuum: An integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions

Density is a key component in the recent surge of mixed-use neighborhood developments. Empirical research has shown an inconsistent picture on the impact of density. In particular, it is unclear whether it is the density or the variables that go long with density that affect people’s travel behavior. Many existing studies on density neglect confounding factors, for example, residential self-selection, generalized travel cost, accessibility, and access to transit stations. In addition, most still use a single trip as their observation unit, even though trip chaining is well recognized. The goal of this paper is to assess the role of density in affecting mode choice decisions in home-based work tours, while controlling for confounding factors. Using the dataset collected in the New York Metropolitan Region, we estimated a simultaneous two-equation system comprising two mutually interacting dependent variables: car ownership and the propensity to use auto. The results confirm the role of density after controlling for the confounding factors; in particular, employment density at work exerts more influence than residential density at home. The study also demonstrates the importance of using tour as the analysis unit in mode choice decisions. The study advances the field by analyzing the role of the built environment on home-based work tours. New knowledge is obtained in the relative contribution of density vs. a set of correlated factors, including generalized travel cost, accessibility, and access to transit stations.

Role of the built environment on mode choice decisions: additional evidence on the impact of density

This paper uses revealed preference and stated preference data collected from a web-based commuter survey in Austin, Texas, to estimate a commute mode choice model. This model accommodates weekly and daily commute and midday stop-making behavior, as well as travel time reliability. A mixed logit framework is used in estimation. The results emphasize the effects of commute and midday stop-making on commute mode choice. The results also indicate that travel time reliability is an important variable in commute mode choice decisions. The paper applies the estimated model to predict the potential mode usage of a proposed commuter rail option as well as to examine the impact of highway tolls. More generally, the mode choice model can be used to examine a whole range of travel mode-related policy actions for the Austin metropolitan region.

/pdf/the-impact-of-stop-making-and-travel-time-reliability-on-kaya0mlmo7.pdf

The Impact of Stop-Making and Travel Time Reliability on Commute Mode Choice

This paper investigates the relationship between mode choice and the complexity of trip chaining patterns. An understanding of the causality between these two choice behaviors may aid in the development of tour-based travel demand modeling systems that attempt to incorporate models of trip chaining and mode choice. The relationship between these two aspects of travel behavior is represented in this paper by considering three different causal structures: one structure in which the trip chaining pattern is determined first and influences mode choice, another structure in which mode choice is determined first and influences the complexity of the trip chaining pattern, and a third structure in which neither is predetermined but both are determined simultaneously. The first two structures are estimated within a recursive bivariate probit modeling framework that accommodates error covariance. The simultaneous logit model is estimated for the third structure that allows a bidirectional simultaneous causality. The analysis and model estimation are performed separately for work tour and non-work tour samples drawn from the 2000 Swiss Microcensus travel survey. Model estimation results show that the causal structure in which trip chain complexity precedes mode choice performs best for both work and non-work tour samples. The structure in which mode choice precedes trip chaining pattern choice was found to give significantly inferior goodness-of-fit measures. These findings have important implications for the development of activity-based and tour-based modeling systems and for the design and planning of public transport systems.

/pdf/an-exploration-of-the-relationship-between-mode-choice-and-rgg0zns2oc.pdf

An Exploration of the Relationship Between Mode Choice and Complexity of Trip Chaining Patterns

The Garisenda Tower and the Asinelli Tower, also widely known as the Two Towers, are the best preserved and famous medieval towers in the city of Bologna (Northern Italy). Standing one close to the other, right in the heart of the city centre, the Two Towers are delicate remains of the old towered city, which counted more than 75 towers in the 12th century.The foundations of historic towers and the surrounding soil often hide major hazards for the long-term preservation of these heritage structures. The initial fundamental step to this aim is indeed a deep understanding of their original conception, foundations and subsoil. However, the idea that also such elements are an integral part of the overall structure, and thus subjected to the same conservation rules, is relatively new. The present paper outlines the investigation criteria applied to the soil-foundation systems of the Two Towers of Bologna and describes the authenticity of their characteristics, through the interpretation of new experimental data and the analysis of historical documents. A geotechnical perspective on this type of monuments turns out to be crucial in order to effectively understand the soil-structure interaction mechanisms, which govern their safety conditions over time. This study also aims to better understand the reasons why the Two Towers of Bologna, despite their numerous similarities, have reached completely different structural configurations.The methodology described to investigate this case study, which required the integration of several aspects, can be usefully applied to any historic tower. 

Learning from a Well-documented Geotechnical Cold Case: The Two Towers of Bologna, Italy

Cone penetration tests (CPT) have been widely used for soil stratification in geotechnical site investigation for decades. However, due to time and budget limits, the layout of CPT sounding at a specific project site is often sparse, leading to significant interpolation uncertainty in the development of subsurface soil 2D cross-section, particularly at locations without CPT measurements. Such development is often com­ bined with empirical classification criteria, which further introduce model uncertainty to soil stratification. These uncertainties may pose great risks to the geotechnical engineering practice. A Bayesian supervised learn­ ing method is presented in this paper for probabilistic soil stratification in a 2D cross-section using limited CPT. The proposed method can not only automatically stratify soils in a 2D cross-section from limited CPT soundings, but also can properly quantify the associated uncertainties. Complete 2D CPT data cross-section is firstly learned from limited number of 1D CPT profiles using Bayesian supervised learning. The associated interpolation uncertainty is modelled numerically using non-parametric random field simulation based on the results of Bayesian supervised learning. Parametric autocorrelation function of CPT data along either vertical or horizontal direction is not needed. A probabilistic model is also developed to account for the model uncer­ tainty of an empirical soil behavior type classification chart. The interpolation uncertainty and soil classifica­ tion model uncertainty are then evaluated simultaneously in a Monte Carlo simulation framework. A simulated data example is used for illustration. The results suggest that the proposed method performs well.

Bayesian supervised learning of 2D subsurface soil stratigraphy using limited cone penetration tests with consideration of uncertainty; Cone Penetration Testing 2022; First Edition

Giovanni Gottardi

Papers

An Exploration of the Relationship Between Mode Choice and Complexity of Trip Chaining Patterns

Learning from a Well-documented Geotechnical Cold Case: The Two Towers of Bologna, Italy

Bayesian supervised learning of 2D subsurface soil stratigraphy using limited cone penetration tests with consideration of uncertainty; Cone Penetration Testing 2022; First Edition