Karthik C. Konduri

Integrated Land Use-Transport Model System with Dynamic Time-Dependent Activity-Travel Microsimulation

The development of integrated land use–transport model systems has long been of interest because of the complex interrelationships between land use, transport demand, and network supply. This paper describes the design and prototype implementation of an integrated model system that involves the microsimulation of location choices in the land use domain, activity–travel choices in the travel demand domain, and individual vehicles on networks in the network supply modeling domain. Although many previous applications of integrated transport demand–supply models have relied on a sequential coupling of the models, the system presented in this paper involves a dynamic integration of the activity–travel demand model and the dynamic traffic assignment and simulation model with appropriate feedback to the land use model system. The system has been fully implemented, and initial results of model system runs in a case study test application suggest that the proposed model design provides a robust behavioral framework for simulation of human activity–travel behavior in space, time, and networks. The paper provides a detailed description of the design, together with results from initial test runs.

A simultaneous equations model of crash frequency by severity level for freeway sections

This paper presents a simultaneous equations model of crash frequencies by severity level for freeway sections using five-year crash severity frequency data for 275 multilane freeway segments in the State of Washington. Crash 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 crash severity on such facilities. This paper applies a joint Poisson regression model with multivariate normal heterogeneities using the method of Maximum Simulated Likelihood Estimation (MSLE). MSLE serves as a computationally viable alternative to the Bayesian approach that has been adopted in the literature for estimating multivariate simultaneous equations models of crash frequencies. The empirical results presented in this paper suggest the presence of statistically significant error correlations across crash frequencies by severity level. The significant error correlations point to the presence of common unobserved factors related to driver behavior and roadway, traffic and environmental characteristics that influence crash frequencies of different severity levels. It is found that the joint Poisson regression model can improve the efficiency of most model coefficient estimators by reducing their standard deviations. In addition, the empirical results show that observed factors generally do not have the same impact on crash frequencies at different levels of severity.

Modeling the connection between activity-travel patterns and subjective well-Being

Transportation models are currently unable to reflect adequately the impacts of policy and investment decisions on peoples well-being and overall quality of life. This paper presents a multivariate ordered-response probit model that is able to capture the influence of activity-travel characteristics on subjective well-being while accounting for unobserved individual traits and attitudes that predispose people in relation to their emotional feelings.

Modeling the Influence of Family, Social Context, and Spatial Proximity on Use of Nonmotorized Transport Mode

This paper presents a joint model of the duration of walking and bicycling activity with the use of a hazard-based specification that recognizes the interval nature of time reported in activity–travel surveys. The model structure takes the form of a multilevel hazard-based model system that accounts for the range of interactions and spatial effects that might affect walking and bicycling mode use. In addition to the individual-specific factors, family (household-specific) interactions, social group (peer) influences, and spatial clustering effects are considered potential factors that contribute to heterogeneity in nonmotorized transport mode use behavior. The model system presented is capable of accommodating grouped duration responses often encountered in activity–travel surveys. A composite marginal likelihood estimation approach is adopted to estimate parameters in a computationally tractable manner. The model system is applied to a survey sample drawn from the recent 2009 National Household Travel Survey in the United States. Model results show that significant unobserved family-level, social group, and spatial proximity effects contribute to heterogeneity in walking and bicycling activity duration. The unobserved effects were also found to have a differential impact on bicycling activity duration, thus suggesting the need to treat and model walking and bicycling separately in transportation modeling systems.

Joint Analysis of Time Use and Consumer Expenditure Data: Examination of Two Approaches to Deriving Values of Time

Estimating the value of time is of considerable interest to transportation professionals charged with evaluating infrastructure investments. Two approaches used to calculate the value of time are the microeconomic utility theory approach and the structural equations modeling method. In an effort to clarify the interpretation and the relationship between the values of time derived from these two approaches, both models were applied to a synthesized data set of one-person, one-worker households created by merging records from the 2008 American Time Use Survey data set with records from the 2008 Consumer Expenditure Survey data set of the United States. The microeconomic model results show that people in the sample data set work until the marginal utility of work is nearly zero. This finding implies that the value of leisure is nearly equal to the wage rate. Comparisons of model parameter estimates between the microeconomic model and the structural equations model suggest that the models offer vastly different measures of the value of leisure. Although the microeconomic model offers a utilitarian measure suitable for computing user benefits, the structural equations model provides a much smaller value of leisure, implying that it is a measure of the willingness to pay as represented by the average relationship between monetary expenditure and time allocation.

Application of Socioeconomic Model System for Activity-Based Modeling

This paper presents results from the application of a comprehensive socioeconomic and demographic model system in conjunction with a continuous-time, activity-based microsimulation model of travel demand developed for the Southern California Association of Governments. The socioeconomic model system includes two major components. The first is a synthetic population generator that is capable of synthesizing a representative population for the entire region while controlling for both household- and person-level marginal distributions. The second is an econometric microsimulator that models various socioeconomic and demographic attributes for each person in the synthetic population with a view to developing a rich set of input data for the activity-based microsimulation model system. The results show that the socioeconomic model system is capable of replicating known distributions of demographic attributes in the population and can be easily scaled for implementation in large regions such as the Southern California area, which includes a population of more than 18 million people in its model boundaries.

Joint Model of Vehicle Type Choice and Tour Length

Tour-based microsimulation model systems are increasingly being applied to the forecasting of travel demand. This paper examines the relationship between two dimensions of tours: the type of vehicle (in a household that owns multiple vehicles of different types) chosen to undertake the tour and the overall length (distance traveled) of the tour. These two dimensions are of much interest in the current planning context, in which concerns about energy sustainability and greenhouse gas emissions are motivating planners to seek ways to mitigate the adverse impacts of automotive travel. Moreover, virtually all tour-based models currently used do not explicitly account for choice of vehicle type in the modeling of tour attributes, despite the critical importance of the choice of vehicle type for energy and emissions analysis. This paper presents a joint discrete–continuous model of choice of vehicle type and length of tours. Estimation results suggested that significant common unobserved factors affected vehicle type choice and length of tours. These factors justified the use of modeling approaches with joint simultaneous equations to model tour attributes. The model specification in which vehicle type choice affected tour length performed better than the specification in which tour length affected vehicle type choice. This outcome suggested that choice of vehicle type (and allocation to household members) was a longer-term choice that influenced shorter-term tour-length choices.

Simulator of activities, greenhouse emissions, networks, and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings, and integration plans

In this paper we describe the recently developed large scale spatio-temporal simulator of activities and travel for Southern California. The simulator includes population synthesis that recreates the entire resident population in this Mega region, provides locations for residences, workplaces, and schools for each person, estimates car ownership and type, and provides other key personal and household characteristics. Then, a synthetic schedule generator recreates for each resident person in the simulated region a schedule of activities and travel that reflects intra-household activity coordination for a day. These synthetic activity and travel daily schedules are then converted to multiple Origin Destination (OD) matrices at different times in a day. These are in turn combined with other OD matrices (representing truck travel, travel from and to ports and airports, and travel generated outside the region) and assigned to the network. The assignment output is then used in the software EMFAC to produce estimates of fuel consumed and pollutants emitted (including CO2) by different classes of vehicles. The overall model system also includes provision for finer spatial and temporal resolutions and a staged plan to implement them. Numerical examples from each major modeling group are also provided together with next steps.

Copula-Based Joint Model of Injury Severity and Vehicle Damage in Two-Vehicle Crashes

In the transportation safety field, in an effort to improve safety, statistical models are developed to identify factors that contribute to crashes as well as those that affect injury severity. This study contributes to the literature on severity analysis. Injury severity and vehicle damage are two important indicators of severity in crashes and are typically modeled independently. However, there are common observed and unobserved factors affecting the two crash indicators that lead to potential interrelationships. Failure to account for the interrelationships between the indicators may lead to biased coefficient estimates in crash severity prediction models. The focus of this study was to explore interrelationships between injury severity and vehicle damage and to also identify the nature of these correlations across different types of crashes. A copula-based methodology that could simultaneously model injury severity and vehicle damage while also accounting for the interrelationships between the two indicators was employed. Furthermore, parameterization of the copula structure was used to represent the interrelationships between the crash indicators as a function of crash characteristics. In this study, six specifications of the copula model—Gaussian, Farlie–Gumbel–Morgenstern, Frank, Clayton, Joe, and Gumbel—were developed. On the basis of goodness-of-fit statistics, the Gaussian copula model was found to outperform the other copula-based model specifications. Results indicated that interrelationships between injury severity and vehicle damage varied with different crash characteristics including manner of collision and collision type.

Exploration of Time Use Utility Derived by Older Individuals from Daily Activity-Travel Patterns

With the growing population of older persons around the world, much attention is being paid to designing transportation systems and built environments that meet the mobility needs and desires of this aging population. On the basis of evidence from travel surveys showing that individuals in older age brackets engage in fewer activities outside the home, there is growing concern that aging persons are increasingly at risk of experiencing social exclusion and diminished quality of life. In this study, the activity time allocation patterns of those 65 years of age and older are compared with those of other age groups to understand better the extent to which older persons may be suffering from diminished levels of satisfaction with their daily activity pattern. Using an activity-based time use utility measure, this study quantifies the amount of welfare or satisfaction that individuals derive from their activity–travel pattern. It is found that older individuals actually exhibit the highest values of time use utility of all age groups; any loss in utility due to diminished out-of-home activity engagement is more than compensated for by gains in utility accrued from the pursuit of discretionary activities at home. The finding challenges the notion that older individuals are experiencing lower levels of satisfaction from their activity patterns but point to the need to design transportation systems that cater to those with physical and mental impairments, regardless of age.