Konstadinos G. Goulias

Multilevel analysis of daily time use and time allocation to activity types accounting for complex covariance structures using correlated random effects

In this paper multilevel analysis is used to study individual choices of time allocation to maintenance, subsistence, leisure, and travel time exploiting the nested data hierarchy of households, persons, and occasions of measurement. The multilevel models in this paper examine the joint and multivariate correlation structure of four dependent variables in a cross-sectional and longitudinal way. In this way, observed and unobserved heterogeneity are estimated using random effects at the household, person, and temporal levels. In addition, random coefficients associated with explanatory variables are also estimated and correlated with these random effects. Using the wide spectrum of options offered by multilevel models to account for individual and group heterogeneity, complex interdependencies among individuals within their households, within themselves over time, and within themselves but across different indicators of behavior, are analyzed. Findings in this analysis include large variance contribution by each level considered, clear evidence of non-linear dynamic behavior in time-allocation, different trajectories of change in time allocation for each of the four dependent variables used, and lack of symmetry in change over time characterized by different trajectories in the longitudinal evolution of each dependent variable. In addition, the multivariate correlation structure among the four dependent variables is different at each of the three levels of analysis.

Transportation systems planning : methods and applications

Introduction TRANSPORTATION SYSTEMS AND THEORIES OF HUMAN BEHAVIOR Transportation Systems Planning, Konstadinos G. Goulias Time Use and Travel Behaviour in Space and Time, Ram M. Pendyala Spatial Behaviour in Transportation Modeling and Planning, Reginald G. Golledge and Tommy Garling Freight Transportation Planning: Models and Methods, Frank Southworth Land Use - Transportation Modeling, Eric J. Miller Land Use Planning, Household Travel, and Household Lifestyles, Kevin J. Krizek DATA COLLECTION AND ANALYSIS Interactive Methods for Activity Scheduling Processes, Sean T. Doherty Statistical and Econometric Data Analysis, Konstadinos G. Goulias Multilevel Statistical Models, Konstadinos G. Goulias Random Utility-Based Discrete Choice Models for Travel Demand Analysis, Chandra R. Bhat Structural Equation Modeling, Thomas F. Golob TRANSPORATATION SYSTEMS SIMULATION AND APPLICATIONS Microsimulation, Eric J. Miller Mobile Source Emissions: An Overview of the Regulatory and Modeling Framework, Debbie A. Niemeier Demographic Microsimulation with DEMOS 2000: Design, Validation, and Forecasting, Ashok Sundararajan and Konstadinos G. Goulias Assessing the Effects of Constrained and Unconstrained Policy Scenarios on Activity-Travel Patterns Using a Learning-Based Simulation System, Theo Arentze, Frank Hofman, Henk van Mourik and Harry Timmermans Centre SIM: First generation Model Design, Pragmatic Implementation and Scenarios, JoNette Kuhnau and Konstadinos G. Goulias

A REPEATED CROSS-SECTIONAL EVALUATION OF CAR OWNERSHIP

This paper studies changes in the relationship between household car ownership and income by household type. Ordered response probit models of car ownership are estimated for a sample of households repeatedly at six time points to track the evolution of income elasticities of car ownership over time. Elasticities of car ownership are found to change over time, questioning the existence of a unique equilibrium point between demand and supply that is implicitly assumed in traditional cross-sectional discrete choice car ownership models. Moreover, different household types and households that underwent household type transitions showed differing patterns of change in elasticities. Observed trends in car ownership and income clearly show behavioral asymmetry where the elasticity of procuring an additional car is greater than that of disposing a car. This too shows the inadequacy of traditional cross-sectional models of car ownership which tend to predict symmetry in behavior. The study suggests the importance of incorporating dynamic trends into the forecasting process, which can be accomplished through the use of longitudinal data.

Development of Indicators of Opportunity-Based Accessibility

“Accessibility,” defined as the ease (or difficulty) with which opportunities for activity can be reached from a given location, can be measured with the cumulative amount of opportunities from an origin within a given amount of travel time. These indicators can be used in regional planning and modeling efforts to integrate land use and travel demand, and an attempt should be made to calculate these indicators for the smallest geographic area. The primary objective of this paper is to illustrate the creation of realistic space-sensitive and time-sensitive block-level accessibility indicators to track the availability of opportunities. These indicators support the development of an activity-based travel demand model by Southern California Association of Governments to provide second-by-second and parcel-by-parcel modeling and simulation. The indicators also provided the base information for mapping opportunities of access to 15 types of industries at different times during a day. The indicators and their maps were defined for the entire region of Southern California through largely available data that included the Census Transportation Planning Package, Dun & Brad-street postprocessed data, detailed highway networks and travel times from the four-step regional models, and arrival and departure times of workers by industry.

Effects of Land Use Characteristics on Residence and Employment Location and Travel Behavior of Urban Adult Workers

The relationships between socioeconomic and demographic characteristics, land use characteristics around the residence and work locations, and a variety of travel behavior indicators are examined by using a structural equations model. This simultaneous equations system allows one to model the effects of land use characteristics on travel behavior while controlling for self-selection bias: certain types of persons choose to live and work in areas that suit their lifestyles and resources. In the model, travel behavior choices are multidimensional; total time away from home, trips and trip distances by three types of modes, car ownership, and possession of a transit pass are included. Land use is captured in geographic information system-based measures of land use and transport supply variables centered on both home and work locations. These measures are reduced to eight land use factors. The analysis provides strong evidence in favor of using land use and urban form designs and planning both around resident...

Predicting Operating Speeds on Low-Speed Urban Streets: Regression and Panel Analysis Approaches

This study compares different statistical approaches to modeling the geometric and driver effects on operating speeds along low-speed urban streets. Linear regression on speed data obtained through data aggregation, linear regression on individual speed data, and panel analysis are discussed. Data collected from ongoing research examining operating speed on low-speed urban streets were modeled by the three techniques. The findings of the modeling techniques are compared and their influence on predicting probable operating speeds of a facility are presented. Traditionally, empirical analysis of operating speed has relied on regression models, using descriptive statistics such as 85th-percentile speed or mean speed to describe the data. This study demonstrates how the use of descriptive statistics obtained through data aggregation misleadingly reduces the total variability and nature of the variability associated with the statistical relationship. The fit of the regression function may appear to be increased, but the influence of the geometric elements may be overstated or understated. Data aggregation also affects inferential and prediction measures. Predictions from models based on aggregate data may appear to be more precise, but this does not imply that they are more reliable. Regression models of speed choice at a specific location within the roadway alignment may explain the effect of geometry but may not capture the effect of individual driver speed choice. As demonstrated in this study, the individual driver effect and geometric variable effect are important. The preliminary conclusion is that the drivers speed choice is highly dependent on roadway geometry and individual driver behavior.

Application of Poisson Regression Models to Activity Frequency Analysis and Prediction

The Poisson regression model and its variants are used to estimate individuals’ daily activity frequencies by activity type using the first 4 years of the Puget Sound Transportation Panel. The estimated model coefficients are applied to the 5th-year observed data to predict daily activity frequencies by activity type. Forecasting accuracy is measured with indicators of deviation between observed and predicted values. Examples of model estimates and their forecasting performance are provided. Comparisons between the observed and predicted 5th-year data show the predictions to be fairly accurate. Subsistence activity is the most accurate among all types, followed by trip chains, out-of-home leisure, and maintenance activities. The analysis also indicates that different theoretical distributions should be used for different dependent variables.

Model for children's school travel mode choice

Numerous programs aimed at enhancing the choice of bicycling and walking as modes of choice for childrens trips to and from school are being implemented by public agencies around the world. Disaggregate models that can account for the myriad of factors that influence the school mode choice of children are needed to forecast the potential impacts of such programs and policies. This paper presents a model for school mode choice that can capture the unobserved spatial interaction effects that may influence household decision making in choosing a mode of transportation for childrens trips to and from school. For example, households that are geographically close together in a neighborhood may interact or observe one another and be influenced by each others actions. To overcome the computational intractability associated with estimating a discrete choice model with spatial interaction effects, the paper proposes a maximum approximated composite marginal likelihood approach for estimating model parameters. The model is applied to a sample of children in Southern California whose households responded to the 2009 National Household Travel Survey in the United States. Spatial correlation effects are statistically significant, and they arise from interactions among households that are geographically close to one another. The findings suggest that public policy programs aimed at enhancing the use of bicycle and walk modes may have a greater impact if directed toward the local neighborhood level as opposed to a more diffuse regional level.

Parental Attitudes Toward Children Walking and Bicycling to School: Multivariate Ordered Response Analysis

Recent research suggests that, besides traditional sociodemographic and built environment attributes, the attitudes and perceptions of parents toward walking and bicycling play a crucial role in deciding which travel modes children take to school. However, little is known about the factors that shape these parental attitudes. The current study aims to investigate this unexplored avenue of research and to identify the influences on parental attitudes toward children walking and bicycling to school as part of a larger nationwide effort to make children more physically active and combat rising trends of childhood obesity in the United States. Through the use of a multivariate ordered response model (a model structure that allows different attitudes to be correlated), the current study analyzes five parental attitudes toward children walking and bicycling to school on the basis of data drawn from the California add-on sample of the 2009 National Household Travel Survey. In particular, the subsample from the Los Angeles–Riverside–Orange County area is used in this study to take advantage of a rich set of microaccessibility measures that are available for this region. It is found that school accessibility, work patterns, current mode use in the household, and sociodemographic characteristics shape parental attitudes toward children walking and bicycling to school. The study findings provide insights on policies, strategies, and campaigns that may help shift parental attitudes to be more favorable toward children walking and bicycling to school.

FRAMEWORK FOR THE ANALYSIS OF GROCERY TELESHOPPING

Household replenishment and consumer direct—two closely related and developing forms of teleshopping that are emerging as strategies within the broader realm of supply chain management—could have an impact on behavior related to grocery shopping trips, as well as on commercial development. In concept, household replenishment and consumer direct are the businesses of delivering groceries to households through various means. These grocery delivery systems have the potential to change household activity behavior, which could result in numerous changes throughout the transportation network. An examination of the relevant issues surrounding implementation of household replenishment and consumer direct, and an analysis of their potential impact on transportation systems planning, are provided. A conceptual framework for modeling changes in business and household behavior is also offered.