Showing papers by "Ram M. Pendyala published in 2008"

Joint Model of Choice of Residential Neighborhood and Bicycle Ownership: Accounting for Self-Selection and Unobserved Heterogeneity

Abstract: This paper presents a joint model of residential neighborhood type choice and bicycle ownership. The objective is to isolate the true causal effects of neighborhood attributes on household bicycle ownership from a spurious association because of residential self-selection effects. The joint model accounts for residential self-selection because of both observed sociodemographic characteristics and unobserved preferences. In addition, the model allows differential residential self-selection effects across different sociodemographic segments. The model was estimated by using a sample of more than 5,000 households from the San Francisco, California, Bay Area. Furthermore, a policy simulation analysis was carried out to estimate the impacts of neighborhood characteristics and sociodemographics on bicycle ownership. The model results show a substantial presence of residential self-selection effects because of observed sociodemographics, such as the number of children, dwelling type, and house ownership. It is s...

An analysis of children’s leisure activity engagement: examining the day of week, location, physical activity level, and fixity dimensions

Abstract: This paper presents a detailed analysis of discretionary leisure activity engagement by children. Children’s leisure activity engagement is of much interest to transportation professionals from an activity-based travel demand modeling perspective, to child development professionals from a sociological perspective, and to health professionals from an active lifestyle perspective that can help prevent obesity and other medical ailments from an early age. Using data from the 2002 Child Development Supplement of the Panel Study of Income Dynamics, this paper presents a detailed analysis of children’s discretionary activity engagement by day of week (weekend versus weekday), location (in-home versus out-of-home), type of activity (physically active versus passive), and nature of activity (structured versus unstructured). A mixed multiple discrete-continuous extreme value model formulation is adopted to account for the fact that children may participate in multiple activities and allocate positive time duration to each of the activities chosen. It is found that children participate at the highest rate and for the longest duration in passive unstructured leisure activities inside the home. Children in households with parents who are employed, higher income, or higher education were found to participate in structured outdoor activities at higher rates. The child activity modeling framework and methodology presented in this paper lends itself for incorporation into larger activity-based travel model systems where it is imperative that children’s activity-travel patterns be explicitly modeled—both from a child health and well-being policy perspective and from a travel forecasting perspective.

Forecasting Statewide Freight Toolkit

Abstract: Federal planning legislation and regulations now mandate that state departments of transportation and metropolitan planning organizations consider the needs of freight when planning and programming transportation investments. While there are standard techniques used to forecast the movement of people, less attention has been paid to forecasting freight movements, and there are consequently fewer standardized techniques that state and local agencies can adapt to their local situation. This Toolkit is designed to provide transportation planners with the information they need to prepare forecasts of freight transportation by highlighting techniques successfully developed by state agencies across the country.

Modeling the Choice Continuum: Integrated Model of Residential Location, Automobile Ownership, Bicycle Ownership, and Commute Tour Mode Choice Decisions

Abstract: The integrated modeling of land use and transportation involves analyzing the entire choice continuum defining people’s lifestyles across temporal scales. This includes long term choices such as residential and work location choices, medium term choices such as vehicle and bicycle ownership, and short term choices such as mode choice and trip departure time choice. In recent times, there has been increasing interest in modeling these choice dimensions simultaneously to account for endogeneity of longer term location choices, residential self-selection effects, and simultaneity of choice processes that define a person’s lifestyle. This paper presents an integrated simultaneous multi-dimensional choice model of residential location, vehicle ownership, bicycle ownership, and commute tour mode. The model system is formulated and estimated on a data set from the San Francisco Bay Area in the United States. Model estimation results show that it is possible to link long term, medium term, and short term choices in a multi-dimensional simultaneous equations model that captures endogeneity and unobserved heterogeneity.

Understanding residential mobility: a joint model of the reason for residential relocation and stay duration

Abstract: tenure and type choices) with short-term travel choices [see, for example, work by Eliasson and Mattsson (2), Waddell et al. (3), and Salon (4); Pinjari et al. (5) provide an extensive listing of such studies]. This stream of research recognizes the possibility that employment, residential, and travel choices are not independent of each other, and that individuals and households adjust with combinations of short-term travel-related and long-term household-related behavioral responses to land use and transportation policies. Similarly, short-term travelrelated experiences may lead to shifts in long-term household choices. For instance, if a worker is living far away from her or his workplace, the household may be more likely in the future to relocate closer to the workplace. Of course, such responses and shifts in long-term housing choices are likely to involve a lag effect, which immediately raises the issue of temporal dynamics. It is not surprising, therefore, that comprehensive model systems of urban systems such as ILUTE (6) and CEMUS (7) include dynamic population microsimulation modules to “evolve” households and individuals, and their spatial locations, over time (to obtain the synthesized population of households and individuals, and their corresponding residential locations, for future years). These model systems involve several dimensions, including in-migration and out-migration from study area, age, mortality, births, employment choices, living arrangement, household formation and dissolution, and household relocation decisions. In this paper, the authors focus on the household relocation decision in particular, including if and when a household will relocate and for what reason.

Validation and Assessment of Activity-Based Travel Demand Modeling Systems

Abstract: The past decade has seen the rapid development of activity- and tour-based travel demand modeling systems. Several metropolitan planning organizations (MPOs) in the United States and metro areas in Europe have implemented such systems to take advantage of the derived nature of travel demand and interdependencies among trips. Despite the appeal of these models, their widespread implementation appears to be hindered by the absence of a detailed validation and assessment of this new wave of model systems. Many MPOs will not adopt such models until they are tested. These sentiments were expressed 10 years ago in New Orleans at the Travel Model Improvement Program (TMIP) Conference on Activity-Based Travel Modeling and more recently in e-mail forums such as the TMIP Listserv. This conference in Austin brings model developers and MPO staff together to discuss validating and assessing activity-based models.

Joint Model of Accident Type and Severity for Two-VehicleCrashes

Abstract: This paper presents a joint model of accident type and severity for two-vehicle crashes. Accident type is characterized by the manner in which two vehicles collide with one another (e.g., head-on, rear-end, sideswipe, etc.) while accident severity is characterized by the traditional ordered hierarchy (property damage only to fatal). While previous work has focused on modeling accident severity as a function of accident type or on modeling accident type alone, there have been virtually no studies aimed at modeling the type of collision and severity level simultaneously. This paper aims to fill this gap in the literature by presenting a joint simultaneous equations model of accident type and severity level using data from the national General Estimates System (GES) in the United States. The simultaneous equations model takes the form of a joint unordered ¨C ordered discrete choice model where collision type is treated as an unordered multinomial discrete variable while severity is treated as an ordered discrete variable. Model estimation is accomplished using full information maximum likelihood methods (FIML) implemented via simulation approaches. Model estimation results show significant presence of error correlations, i.e., the presence of common unobserved factors that influence accident type and severity, thus supporting the jointness in modeling these two accident characteristics.

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 with a view to understand the inter-personal and intra-personal variability in weekly activity engagement at a detailed level. The analysis is motivated by the need to understand discretionary activity engagement on a broader time scale than the typical one-day analysis that pervades the activity-travel behavior literature. A panel version of the Mixed Multiple Discrete Continuous Extreme Value model (MMDCEV) that explicitly accounts for the panel nature of the multi-week activity-travel behavior data set is developed and estimated on the data set. 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.

Simultaneous Equations Model of Accident Frequency bySeverity Level for Freeway Sections

Abstract: This paper presents a simultaneous equations model of accident severity by level for freeway sections using five-year accident severity frequency data for 275 multilane freeway segments in the State of Washington. Accident severity is a subject of much interest in the context of freeway safety due to higher speeds of travel on freeways and the desire of transportation professionals to implement measures that could potentially reduce accident severity on such facilities. While past research has presented univariate frequency models of accident severity, this paper attempts to make a substantive contribution to the field by presenting a simultaneous equations model of accident frequency by severity level using a n-dimensional multivariate poisson regression (MVP) modeling methodology that adopts a multivariate normal heterogeneity specification to capture error correlation structures in the model system. Adopting such a modeling methodology is important as there may be unobserved factors related to driver behavior and roadway/traffic/environmental characteristics that influence accident frequencies at multiple severity levels. The paper presents the model formulation and empirical results of model estimation in the context of the Washington State dataset that suggest that error correlations are statistically significant in modeling accident frequency by severity level. These findings point to the need to adopt simultaneous equations modeling approaches such as that presented in this paper for modeling safety phenomena.

Data-Driven Perspective on Management of Safety Risk at State Agencies: Case Study in Washington State

Abstract: Nationally, transportation agencies have embarked on efforts to collect information digitally on highway attributes to help understand factors that contribute to traffic crash occurrences. Instrumented vehicles, database modeling efforts, and enhancements in crash-data collection are salient examples of such efforts. This paper provides insights into the prospective value of roadway information as it pertains to statistical analysis of severity of crashes. It presents a case study analysis from Washington State that involves divided highway crash data. A statistical model is presented that demonstrates an empirical relationship between key roadway variables and distributions of crash severity. The other notable output of this paper involves the contribution of weather information to the distributions of crash severity. While the case study is restricted to divided highways in the northwest part of the United States, the statistical insights from the analysis of severity distributions indicate the prospective value of key data elements in relation to their regular measurement and updates to statewide crash risk management.