Showing papers by "Ram M. Pendyala published in 2007"

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

Abstract: 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.

Modeling residential sorting effects to understand the impact of the built environment on commute mode choice

Abstract: 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.

Modeling Residential Sorting Effects to Understand the Impact of the Built Environment on Commute Mode Choice

Abstract: This paper examines 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 in 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.

Modeling Interdependence in Household Residence and Workplace Choices

Abstract: Models of residential and workplace location choice prevalent in the literature often assume that one choice dimension is exogenous to the other. But a broad and uniform assumption that one choice dimension is exogenous and influences the other may be too strong to use as the foundation for current behavioral research or applied policy analysis. This paper examines the interdependence of residence and workplace choices and develops a novel approach to modeling these choice dependencies. Two problems related to such joint modeling efforts are addressed. First, through a latent market segment modeling approach, the paper offers a methodology for accommodating different sequential decisionmaking processes that may be present in the population—for example, residential location may be chosen first and may influence workplace location for one segment and vice versa. Second, the modeling approach offers a means of overcoming the exploding choice set problem when attempting to model multidimensional choice phenom...

Understanding Travel Time Expenditures Around the World: Exploring the Notion of a Travel Time Frontier

Abstract: Travel behavior researchers have been intrigued by the amount of time that people allocate to travel in a day, i.e., the daily travel time expenditure, commonly referred to as a “travel time budget”. Explorations into the notion of a travel time budget have once again resurfaced in the context of activity-based and time use research in travel behavior modeling. This paper revisits the issue by developing the notion of a travel time frontier (TTF) that is distinct from the actual travel time expenditure or budget of an individual. The TTF is defined in this paper as an intrinsic maximum amount of time that people are willing to allocate for travel. It is treated as an unobserved frontier that influences the actual travel time expenditure measured in travel surveys. Using travel survey datasets from around the world (i.e., US, Switzerland and India), this paper sheds new light on daily travel time expenditures by modeling the unobserved TTF and comparing these frontiers across international contexts. The stochastic frontier modeling methodology is employed to model the unobserved TTF as a production frontier. Separate models are estimated for commuter and non-commuter samples to recognize the differing constraints between these market segments. Comparisons across the international contexts show considerable differences in average unobserved TTF values.

An analysis of weekly out-of-home discretionary activity participation and time-use behavior

Abstract: Activity-travel behavior research has hitherto focused on the modeling and understanding of daily time use and activity patterns and resulting travel demand. In this particular paper, an analysis and modeling of weekly activity-travel behavior is presented using a unique multi-week activity-travel behavior data set collected in and around Zurich, Switzerland. The paper focuses on six categories of discretionary activity participation to understand the determinants of, and the inter-personal and intra-personal variability in, weekly activity engagement at a detailed level. A panel version of the Mixed Multiple Discrete Continuous Extreme Value model (MMDCEV) that explicitly accounts for the panel (or repeated-observations) nature of the multi-week activity-travel behavior data set is developed and estimated on the data set. The model also controls for individual-level unobserved factors that lead to correlations in activity engagement preferences across different activity types. To our knowledge, this is the first formulation and application of a panel MMDCEV structure in the econometric literature. The analysis suggests the high prevalence of intra-personal variability in discretionary activity engagement over a multi-week period along with inter-personal variability that is typically considered in activity-travel modeling. In addition, the panel MMDCEV model helped identify the observed socio-economic factors and unobserved individual specific factors that contribute to variability in multi-week discretionary activity participation.

Framework of Modeling and Forecasting Stop-Level Transit Patronage

Abstract: This paper presents a framework of modeling and forecasting stop-level transit patronage for service planning. The paper describes this framework in terms of its three most important features in relation to existing frameworks in the literature. It models and forecasts transit boardings at the individual stop level. It separates direct boarding from transfer boarding for both modeling and forecasting, and, it treats inter-relationships in a transit network through measures of accessibility to opportunities for potential activity participation. The paper specifies a model structure that reflects these features, describes the Tri-Met data from Portland, Oregon, and estimates a set of Poison models for both direct and transfer boardings for the weekday morning peak. In addition, the paper discusses both the advantages of this framework and the challenges to its implementation. This framework is in the process of being implemented into T-BEST, a user-friendly software package.

An Analysis of Commute Travel Times in an International Context

Abstract: Extreme commuters, defined as those traveling more than 90 minutes each way to and from work, are one of the fastest growing market segments in the United States. The availability of affordable housing, the concentration and specialization of employment, and transportation access are but some of the factors contributing to this phenomenon. This phenomenon is occurring at a time of unprecedented energy and environmental concerns and there is much interest in slowing or even reversing this trend. Who are these people and what are the socio-economic and demographic factors that are associated with long commutes? What is happening in other countries where land use densities are higher and transit systems are more prevalent? This study aims to answer these questions by recognizing that commute lengths are a manifestation of people’s residential and work location choices. Using data sets from the United States, Switzerland, and India, international comparisons of commute length market segments are made to better understand the prevalence and socio-economic composition of long duration commuters, defined as those commuting 60 minutes or more each way in this paper. The descriptive statistical comparisons and multinomial logit model estimation results confirm hypotheses that lifecycle stage, personal attributes, and household characteristics are strongly correlated with commute length. More importantly, the study findings raise interesting and important questions regarding the traditional notion that higher densities and levels of transit use are associated with shorter commutes. The percent of long-duration commuters is found to be the lowest in the United States, where land use densities and transit use are among the lowest in the world.

Relationships Between Work and Nonwork Activity and Travel Durations in a Developing Country Context

Abstract: Activity-based microsimulation methods aim to model activity-travel patterns at the level of the individual traveler or decision-maker. However, quite often, individual activity-travel patterns and decisions are influenced through interactions with various agents. Models of activity engagement and time allocation should reflect these interactions to accurately portray the effects that such interactions may have on activity-travel patterns. This paper examines interactions between two adult household members in two-adult households using a data set derived from a household travel survey conducted in Thane city in India. Daily work and non-work activity and travel time allocations between two household members are examined and potential trade-offs and complementary effects are modeled simultaneously using a structural equations modeling methodology. The model estimation results offer significant model coefficients and plausible interpretations consistent with the Indian context. The model meaningfully captures strong interactions, joint activity participation and task allocation among household members and its effects in making work and non-work activity and travel time allocation decisions. Also, this research offers a unique opportunity to compare the study findings with a past study carried out on a data set from Southeast Florida. Comparison between a developed and a developing country context facilitates the understanding about how diverse socio-cultural aspects can influence interaction between household adults and its effect on their activity-travel patterns. Finally, the study suggests activity-based approach can provide suitable framework in the development of advanced modeling techniques in the developing countries like India.