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1.
Household vehicle miles of travel (VMT) has been exhibiting a steady growth in post-recession years in the United States and has reached record levels in 2017. With transportation accounting for 27 percent of greenhouse gas emissions, planning professionals are increasingly seeking ways to curb vehicular travel to advance sustainable, vibrant, and healthy communities. Although there is considerable understanding of the various factors that influence household vehicular travel, there is little knowledge of their relative contribution to explaining variance in household VMT. This paper presents a holistic analysis to identify the relative contribution of socio-economic and demographic characteristics, built environment attributes, residential self-selection effects, and social and spatial dependency effects in explaining household VMT production. The modeling framework employs a simultaneous equations model of residential location (density) choice and household VMT generation. The analysis is performed using household travel survey data from the New York metropolitan region. Model results showed insignificant spatial dependency effects, with socio-demographic variables explaining 33 percent, density (as a key measure of built environment attributes) explaining 12 percent, and self-selection effects explaining 11 percent of the total variance in the logarithm of household VMT. The remaining 44 percent remains unexplained and attributable to omitted variables and unobserved idiosyncratic factors, calling for further research in this domain to better understand the relative contribution of various drivers of household VMT. 相似文献
2.
Travel to and from school can have social, economic, and environmental implications for students and their parents. Therefore, understanding school travel mode choice behavior is essential to find policy-oriented approaches to optimizing school travel mode share. Recent research suggests that psychological factors of parents play a significant role in school travel mode choice behavior and the Multiple Indicators and Multiple Causes (MIMIC) model has been used to test the effect of psychological constructs on mode choice behavior. However, little research has used a systematic framework of behavioral theory to organize these psychological factors and investigate their internal relationships. This paper proposes an extended theory of planned behavior (ETPB) to delve into the psychological factors caused by the effects of adults’ cognition and behavioral habits and explores the factors’ relationship paradigm. A theoretical framework of travel mode choice behavior for students in China is constructed. We established the MIMIC model that accommodates latent variables from ETPB. We found that not all the psychological latent variables have significant effects on school travel mode choice behavior, but habit can play an essential role. The results provide theoretical support for demand policies for school travel. 相似文献
3.
The aim of this paper is to develop a methodological framework for the incorporation of social interaction effects into choice models. The developed method provides insights for modeling the effect of social interaction on the formation of psychological factors (latent variables) and on the decision-making process. The assumption is based on the fact that the way the decision maker anticipates and processes the information regarding the behavior and the choices exhibited in her/his social environment, affects her/his attitudes and perceptions, which in turn affect her/his choices. The proposed method integrates choice models with decision makers’ psychological factors and latent social interaction. The model structure is simultaneously estimated providing an improvement over sequential methods as it provides consistent and efficient estimates of the parameters. The methodology is tested within the context of a household aiming to identify the social interaction effects between teenagers and their parents regarding walking-loving behavior and then the effect of this on mode to school choice behavior. The sample consists of 9,714 participants aged from 12 to 18 years old, representing 21 % of the adolescent population of Cyprus. The findings from the case study indicate that if the teenagers anticipate that their parents are walking lovers, then this increases the probability of teenagers to be walking-lovers too and in turn to choose walking to school. Generally, the findings from the application result in: (a) improvements in the explanatory power of choice models, (b) latent variables that are statistically significant, and (c) a real-world behavioral representation that includes the social interaction effect. 相似文献
4.
Abdul Rawoof Pinjari Ram M. Pendyala Chandra R. Bhat Paul A. Waddell 《Transportation》2011,38(6):933-958
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. 相似文献
5.
This paper formulates a generalized heterogeneous data model (GHDM) that jointly handles mixed types of dependent variables—including multiple nominal outcomes, multiple ordinal variables, and multiple count variables, as well as multiple continuous variables—by representing the covariance relationships among them through a reduced number of latent factors. Sufficiency conditions for identification of the GHDM parameters are presented. The maximum approximate composite marginal likelihood (MACML) method is proposed to estimate this jointly mixed model system. This estimation method provides computational time advantages since the dimensionality of integration in the likelihood function is independent of the number of latent factors. The study undertakes a simulation experiment within the virtual context of integrating residential location choice and travel behavior to evaluate the ability of the MACML approach to recover parameters. The simulation results show that the MACML approach effectively recovers underlying parameters, and also that ignoring the multi-dimensional nature of the relationship among mixed types of dependent variables can lead not only to inconsistent parameter estimation, but also have important implications for policy analysis. 相似文献
6.
An understanding of the key factors influencing bicycle commuting is essential for developing effective policies towards a cyclable city. This paper contributes to this line of research by proposing a methodology for including cycling-related indicators in mobility surveys based on the Theory of Planned Behaviour (TPB), and applying an exploratory factor analysis (EFA) to evaluate the structure of latent variables associated with bicycle commuting. The EFA identified six cycling latent variables: Lifestyle, Safety and comfort, Awareness, Direct disadvantages, Subjective norm, and Individual capabilities. These were complemented with a latent variable related to habit: Non-commuting cycling habit. Statistical differences and regression analysis were applied with the cycling latent variables. The study also includes the relationship between objective factors and bicycle commuting, which reveals minor associations. This methodology was applied to the “starter cycling city” of Vitoria-Gasteiz (Spain). The results confirm that in this context – in transition to a cyclable city – safety and comfort issues are not the main barriers for all commuters, although more progress needs to be made to normalise cycling. A set of customised policy initiatives is recommended in the light of the research findings, including marketing campaigns to encourage non-commuting cycling trips, bicycle measures to target social groups as opposed to individuals, bicycle-specific programs such as “Bike-to-work Days”, and cycling courses. 相似文献
7.
Modeling residential sorting effects to understand the impact of the built environment on commute mode choice 总被引:5,自引:2,他引:3
Abdul Rawoof Pinjari Ram M. Pendyala Chandra R. Bhat Paul A. Waddell 《Transportation》2007,34(5):557-573
This paper presents an examination of the significance of residential sorting or self selection effects in understanding the
impacts of the built environment on travel choices. Land use and transportation system attributes are often treated as exogenous
variables in models of travel behavior. Such models ignore the potential self selection processes that may be at play wherein
households and individuals choose to locate in areas or built environments that are consistent with their lifestyle and transportation
preferences, attitudes, and values. In this paper, a simultaneous model of residential location choice and commute mode choice
that accounts for both observed and unobserved taste variations that may contribute to residential self selection is estimated
on a survey sample extracted from the 2000 San Francisco Bay Area household travel survey. Model results show that both observed
and unobserved residential self selection effects do exist; however, even after accounting for these effects, it is found
that built environment attributes can indeed significantly impact commute mode choice behavior. The paper concludes with a
discussion of the implications of the model findings for policy planning.
相似文献
Paul A. WaddellEmail: |
8.
This paper proposes a reformulation of count models as a special case of generalized ordered-response models in which a single latent continuous variable is partitioned into mutually exclusive intervals. Using this equivalent latent variable-based generalized ordered response framework for count data models, we are then able to gainfully and efficiently introduce temporal and spatial dependencies through the latent continuous variables. Our formulation also allows handling excess zeros in correlated count data, a phenomenon that is commonly found in practice. A composite marginal likelihood inference approach is used to estimate model parameters. The modeling framework is applied to predict crash frequency at urban intersections in Arlington, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files between 2003 and 2009, resulting in 1190 intersection-year observations. The results reveal the presence of intersection-specific time-invariant unobserved components influencing crash propensity and a spatial lag structure to characterize spatial dependence. Roadway configuration, approach roadway functional types, traffic control type, total daily entering traffic volumes and the split of volumes between approaches are all important variables in determining crash frequency at intersections. 相似文献
9.
Emerging autonomous vehicles (AVs) and shared mobility systems per se will transform urban passenger transportation. Coupled together, shared AVs (SAVs) can facilitate widespread use of shared mobility services by providing flexible public travel modes comparable to private AV. Hence, it may be conjectured that future urban mobility is likely an on-demand service and AV private ownership is unappealing. Nonetheless, it is still unclear what observable and latent factors will drive public interest in (S)AVs, the answer to which will have important implications on transportation system performance. This paper aims to jointly model public interest in private AVs and multiple SAV configurations (carsharing, ridesourcing, ridesharing, and access/egress mode) in daily and commute travels with explicit treatment of the correlations across the (S)AV types. To this end, multivariate ordered outcome models with latent variables are employed, whereby latent attitudes and preferences describing traveler safety concern about AV, green travel pattern, and mobility-on-demand savviness are accounted for using structural and measurement equations. Drawing from a stated preference survey in the State of Washington, important insights are gained into the potential user groups based on the socio-economic, built environment, and daily/commute travel behavior attributes. Key policies are also offered to promote public interest in (S)AVs by scrutinizing the marginal effects of the latent variables. 相似文献
10.
This study adopts a dwelling unit level of analysis and considers a probabilistic choice set generation approach for residential choice modeling. In doing so, we accommodate the fact that housing choices involve both characteristics of the dwelling unit and its location, while also mimicking the search process that underlies housing decisions. In particular, we model a complete range of dwelling unit choices that include tenure type (rent or own), housing type (single family detached, single family attached, or apartment complex), number of bedrooms, number of bathrooms, number of storeys (one or multiple), square footage of the house, lot size, housing costs, density of residential neighborhood, and commute distance. Bhat’s (2015) generalized heterogeneous data model (GHDM) system is used to accommodate the different types of dependent outcomes associated with housing choices, while capturing jointness caused by unobserved factors. The proposed analytic framework is applied to study housing choices using data derived from the 2009 American Housing Survey (AHS), sponsored by the Department of Housing and Urban Development (HUD) and conducted by the U.S. Census Bureau. The results confirm the jointness in housing choices, and indicate the superiority of a choice set formation model relative to a model that assumes the availability of all dwelling unit alternatives in the choice set. 相似文献
11.
Abstract Hybrid choice modelling approaches allow latent variables in mode choice utility functions to be addressed. However, defining attitude and behavior as latent variables is influenced by the researcher's assumptions. Therefore, it is better to capture the effects of latent behavioral and attitudinal factors as latent variables than defining behaviors and attitudes per se. This article uses a hybrid choice model for capturing such latent effects, which will herein be referred to as modal captivity effects in commuting mode choice. Latent modal captivity refers to the unobserved and apparently unexplained attraction towards a specific mode of transportation that is resulting from latent attitude and behavior of passengers in addition to the urban transportation system. In empirical models, the latent modal captivity variables are explained as functions of different observed variables. Empirical models show significant improvement in fitting observed data as well as improved understanding of travel behavior. 相似文献
12.
The focus of this paper is to learn the daily activity engagement patterns of travelers using Support Vector Machines (SVMs), a modeling approach that is widely used in Artificial intelligence and Machine Learning. It is postulated that an individual’s choice of activities depends not only on socio-demographic characteristics but also on previous activities of individual on the same day. In the paper, Markov Chain models are used to study the sequential choice of activities. The dependencies among activity type, activity sequence and socio-demographic data are captured by employing hidden Markov models. In order to learn model parameters, we use sequential multinomial logit models (MNL) and multiclass Support Vector Machines (K-SVM) with two different dependency structures. In the first dependency structure, it is assumed that type of activity at time ‘t’ depends on the last previous activity and socio-demographic data, whereas in the second structure we assume that activity selection at time ‘t’ depends on all of the individual’s previous activity types on the same day and socio-demographic characteristics. The models are applied to data drawn from a set of California households and a comparison of the accuracy of estimation of activity types and their sequence in the agenda, indicates the superiority of K-SVM models over MNL. Additionally, we show that accuracy in estimating activity patterns increases using different sets of explanatory variables or tuning parameters of the kernel function in K-SVM. 相似文献
13.
In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering
the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of
the determinants of route choice behavior by proposing and estimating a hybrid model that integrates latent variable and route
choice models. Data contain information about latent variable indicators and chosen routes of travelers driving regularly
from home to work in an urban network. Choice sets include alternative routes generated with a branch and bound algorithm.
A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to
choice indicators, and structural equations, which link travelers’ observable characteristics to latent variables and explanatory
variables to utilities. Estimation results illustrate that considering latent variables (i.e., memory, habit, familiarity,
spatial ability, time saving skills) alongside traditional variables (e.g., travel time, distance, congestion level) enriches
the comprehension of route choice behavior. 相似文献
14.
In the current paper, we propose the use of a multivariate skew-normal (MSN) distribution function for the latent psychological constructs within the context of an integrated choice and latent variable (ICLV) model system. The multivariate skew-normal (MSN) distribution that we use is tractable, parsimonious in parameters that regulate the distribution and its skewness, and includes the normal distribution as a special interior point case (this allows for testing with the traditional ICLV model). Our procedure to accommodate non-normality in the psychological constructs exploits the latent factor structure of the ICLV model, and is a flexible, yet very efficient approach (through dimension-reduction) to accommodate a multivariate non-normal structure across all indicator and outcome variables in a multivariate system through the specification of a much lower-dimensional multivariate skew-normal distribution for the structural errors. Taste variations (i.e., heterogeneity in sensitivity to response variables) can also be introduced efficiently and in a non-normal fashion through interactions of explanatory variables with the latent variables. The resulting model we develop is suitable for estimation using Bhat’s (2011) maximum approximate composite marginal likelihood (MACML) inference approach. The proposed model is applied to model bicyclists’ route choice behavior using a web-based survey of Texas bicyclists. The results reveal evidence for non-normality in the latent constructs. From a substantive point of view, the results suggest that the most unattractive features of a bicycle route are long travel times (for commuters), heavy motorized traffic volume, absence of a continuous bicycle facility, and high parking occupancy rates and long lengths of parking zones along the route. 相似文献
15.
Using latent class cluster analysis, this paper investigates the spatial, social, demographic, and economic determinants of
immigrants’ joint distribution among travel time, mode choice, and departure time for work using the 2000 Census long form
data. Through a latent tree structure analysis, age, residential location, immigration stage, gender, personal income, and
race are found to be the primary determinants in the workplace commute decision-making process. By defining several relatively
homogeneous population segments, the likelihood of falling into each segment is found to differ across age groups and geography,
with different indicators affecting each group differentially. This analysis complements past studies that used regression
models to investigate socio-demographic indicators and their impact on travel behavior in two distinct ways: (a) analysis
is done by considering travel time, mode choice, and departure time for work simultaneously, and (b) heterogeneity in behavior
is accounted for using methods that identify different groups of behavior and then their determinants. Conclusively the method
here is richer than many other methods used to study the ethnically diverse population of California and shows the addition
of geographic location and latent segment identification to greatly improve our understanding of specific behaviors. It also
provides evidence that immigrants are as diverse as the non-immigrant population and transportation policies need to be defined
accordingly.
相似文献
Konstadinos G. GouliasEmail: |
16.
The effect of social comparisons on commute well-being 总被引:1,自引:0,他引:1
Maya Abou-Zeid Moshe Ben-Akiva 《Transportation Research Part A: Policy and Practice》2011,45(4):345-361
We study the effect of social comparisons on travel happiness and behavior. Social comparisons arise from exchanges of information among individuals. We postulate that the social gap resulting from comparisons is a determinant of “comparative happiness” (i.e. happiness arising from comparisons), which in turn affects subsequent behavior. We develop a modeling framework based on the Hybrid Choice Model that captures the indirect effect of social comparisons on travel choices through its effect on comparative happiness.We present an empirical analysis of one component of this framework. Specifically, we study how perceived differences between experienced commute attributes and those communicated by others affect comparative happiness and consequently overall commute satisfaction. We find that greater comparative happiness arising from favorable comparisons of one’s commute to that of others (e.g. shorter commute time than others, same mode as others for car commuters, and different mode than others for non-motorized commuters) increases overall commute satisfaction or utility.The empirical model develops only the link between social comparisons and happiness in the comparisons-happiness-behavior chain. It is anticipated that the theoretical framework that considers the entire chain will enhance the behavioral realism of “black box” models that do not account for happiness in the link between comparisons and behavior. 相似文献
17.
Integrated Choice and Latent Variable (ICLV) models are an increasingly popular extension to discrete choice models that attempt explicitly to model the cognitive process underlying the formation of any choice. This study was born from the discovery that an ICLV model can in many cases be reduced to a choice model without latent variables that fits the choice data at least as well as the original ICLV model from which it was obtained. The failure of past studies to recognize this fact raised concerns about other benefits that have been claimed with regards to the framework. With the objective of addressing these concerns, this study undertakes a systematic comparison between the ICLV model and an appropriately specified reduced form choice model. We derive analytical proofs regarding the benefits of the framework and use synthetic datasets to corroborate any conclusions drawn from the analytical proofs. We find that the ICLV model can under certain conditions lead to an improvement in the analyst's ability to predict outcomes to the choice data, allow for the identification of structural relationships between observable and latent variables, correct for bias arising from omitted variables and measurement error, reduce the variance of parameter estimates, and abet practice and policy, all in ways that would not be possible using the reduced form choice model. We synthesize these findings into a general process of evaluation that can be used to assess what gains, if any, might be had from developing an ICLV model in a particular empirical context. 相似文献
18.
This study gains insight into individual motivations for choosing to own and use autonomous vehicles and develops a model for autonomous vehicle long-term choice decisions. A stated preference questionnaire is distributed to 721 individuals living across Israel and North America. Based on the characteristics of their current commutes, individuals are presented with various scenarios and asked to choose the car they would use for their commute. A vehicle choice model which includes three options is estimated:
- (1)Continue to commute using a regular car that you have in your possession.
- (2)Buy and shift to commuting using a privately-owned autonomous vehicle (PAV).
- (3)Shift to using a shared-autonomous vehicle (SAV), from a fleet of on-demand cars for your commute.
19.
This paper presents a system of hierarchical rule-based models of trip generation and modal split. Travel attributes, like
trip counts for different transportation modes and commute distance, are among the modeled variables. The proposed framework
could be considered as an alternative for several modules of the traditional travel demand modeling approach, while providing
travel attributes at the highly disaggregate level that can be also used in activity-based micro-simulation modeling systems.
Nonetheless, the modeling framework of this study is not considered as a substitute for activity-based models. The explanatory
variables set ranges from socio-economic and demographic attributes of the household to the built environment characteristics
of the household residential location. Another important contribution of the study is a framework in which travel attributes
are modeled in conjunction with each other and the interdependencies among them are postulated through a hierarchical system
of models. All the models are developed using rule-based decision tree method. Moreover, the models developed in this study
present a useful improvement in increasing the practicality and accuracy of the rule-based travel data simulation models. 相似文献
20.
The current article proposes an approach to accommodate flexible spatial dependency structures in discrete choice models in
general, and in unordered multinomial choice models in particular. The approach is applied to examine teenagers’ participation
in social and recreational activity episodes, a subject of considerable interest in the transportation, sociology, psychology,
and adolescence development fields. The sample for the analysis is drawn from the 2000 San Francisco Bay Area Travel Survey
(BATS) as well as other supplementary data sources. The analysis considers the effects of a variety of built environment and
demographic variables on teenagers’ activity behavior. In addition, spatial dependence effects (due to common unobserved residential
neighborhood characteristics as well as diffusion/interaction effects) are accommodated. The variable effects indicate that
parents’ physical activity participation constitutes the most important factor influencing teenagers’ physical activity participation
levels, In addition, part-time student status, gender, and seasonal effects are also important determinants of teenagers’
social-recreational activity participation. The analysis also finds strong spatial correlation effects in teenagers’ activity
participation behaviors. 相似文献