TRANSIMS (Transportation Analysis and Simulation System) developed at the Los Alamos National Laboratory, is an integrated system of travel forecasting models designed to give transportation planners accurate and complete information on traffic impacts, congestion, and pollution. TRANSIMS is a micro-simulation model which uses census data to generate a synthetic population and assigns activities using activity survey data to each person of every household of the synthetic population. The synthetic households generated from the census data are matched with the survey households based on their demographic characteristics. The activities of the survey household individuals are then assigned to the individuals of the matched synthetic households. The CART algorithm is used to match the households. With the use of CART algorithm a classification tree is built for the activity survey households based on some dependent and independent variables from the demographic data. The TRANSIMS model assumes activity times as dependent variables for building the classification tree. The topic of this research is to compare the TRANSIMS approach of using times spent in executing the activities as dependent variables, compared to match the alternative of using travel times for trips between activities as dependent variables i.e. to use the travel time pattern instead of activity time pattern to match the persons in the survey households with the synthetic households. Thus assuming that if the travel time patterns are the same then we can match the survey households to the synthetic population i.e. people with similar demographic characteristics tend to have similar travel time patterns. The algorithm of the Activity Generator module along with the original set of dependent variables, were first used to generate a base case scenario. Further tests were carried out using an alternative set of dependent variables in the algorithm. A sensitivity analysis was also carried out to test the affect of different sets of dependent variables in generating activities using the algorithm of the Activity Generator. The thesis also includes a detailed documentation of the results from all the tests. / Master of Science

Transportation sector has been a major contributing factor to the overall emissions of most pollutants and thus their impacts on the environment. Among all transportation activities, on-road travel accounts for most part of the Greenhouse gas (GHG) emissions and fuel use. It also has a very un-desirable impact on the transportation network conditions increasing the traffic congestion levels. The main aim of transportation planning agencies is to implement the policy changes that will reduce automobile dependency and increase transit and non-motorized modes usage. However, planning agencies can come up with proactive economic, land-use and transportation policies provided they have a model which is sensitive to all the above mentioned factors to predict the vehicle fleet composition and usage of households. Moreover, the type of vehicle that a household gets (vehicle type choice) and the annual mileage (usage) associated with that vehicle is very closely related to the person in the household who uses that vehicle the most (allocation to primary driver). So, it is no longer possible to view all these decisions separately. Instead, we need to model all these decisions- vehicle type choice, usage, and allocation to primary driver simultaneously at a household level. In this study, we estimate and apply a joint household-level model of the number of vehicles owned by the household, the vehicle type choice of each vehicle, the annual mileage on each vehicle, as well as the individual assigned as the primary driver for each vehicle. A version of the proposed model system currently serves as the engine for a household vehicle composition and evolution simulator, which itself has been embedded within the larger SimAGENT (for Simulator of Activities, Greenhouse emissions, Networks, and Travel) activity-based travel and emissions forecasting system for the Southern California Association of Governments (SCAG) planning region. / text

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This study presents an analytical model of the interaction between urban form and the demand for transit travel, in which residential location, transit demand, and the spatial dispersion of non-work activities are endogenously determined. In this model, travel demand is considered a derived demand brought about by the necessity to engage in out-ofhome activities whose geographical extent is affected by urban form. In a departure from the urban monocentric model, residential location is defined as a job-residence pair in an urban area in which jobs, residences, and non-work activities are dispersed. Transit demand is then determined by residential location, work trips, non-work trip chains, and goods consumption. Theoretically derived hypotheses are empirically tested using a dataset that integrates travel and land-use data. There is evidence of a significant influence of land-use patterns on transit patronage. In turn, transit demand affects consumption and non-work travel. Although much reliance has been placed on population density as a determinant of transit demand, it is found here that population density does not have a large impact on transit demand and, moreover, that the effect decreases when residential location is endogenous. To increase transit use, urban planners have advocated a mix of residential and commercial uses in proximity to transit stations. In this study, it is found that the importance of transit-station proximity is weakened by idiosyncratic preferences for residential location. In addition, when population density and residential location are jointly endogenous, the elasticity of transit demand with respect to walking distance to a transit station decreases by about 33 percent over the case in which these variables are treated an exogenous. The research reported here is the first empirical work that explicitly relates residential location to trip chaining in a context in which individuals jointly decide residential location and the trip chain. If is found that households living farther from work use less transit and that trip-chaining behavior explains this finding. Households living far from work engage in complex trip chains and have, on average, a more dispersed activity space, which requires reliance on more flexible modes of transportation. Therefore, reducing the spatial allocation of non-work activities and improving transit accessibility at and around subcenters would increase transit demand. Similar effects can be obtained by increasing the presence of retail locations in proximity to transit-oriented households. Although focused on transit demand, the framework can be easily generalized to study other forms of travel.

abstract: Rapid developments are occurring in the arena of activity-based microsimulation models. Advances in computational power, econometric methodologies and data collection have all contributed to the development of microsimulation tools for planning applications. There has also been interest in modeling child daily activity-travel patterns and their influence on those of adults in the household using activity-based microsimulation tools. It is conceivable that most of the children are largely dependent on adults for their activity engagement and travel needs and hence would have considerable influence on the activity-travel schedules of adult members in the household. In this context, a detailed comparison of various activity-travel characteristics of adults in households with and without children is made using the National Household Travel Survey (NHTS) data. The analysis is used to quantify and decipher the nature of the impact of activities of children on the daily activity-travel patterns of adults. It is found that adults in households with children make a significantly higher proportion of high occupancy vehicle (HOV) trips and lower proportion of single occupancy vehicle (SOV) trips when compared to those in households without children. They also engage in more serve passenger activities and fewer personal business, shopping and social activities. A framework for modeling activities and travel of dependent children is proposed. The framework consists of six sub-models to simulate the choice of going to school/pre-school on a travel day, the dependency status of the child, the activity type, the destination, the activity duration, and the joint activity engagement with an accompanying adult. Econometric formulations such as binary probit and multinomial logit are used to obtain behaviorally intuitive models that predict children's activity skeletons. The model framework is tested using a 5% sample of a synthetic population of children for Maricopa County, Arizona and the resulting patterns are validated against those found in NHTS data. Microsimulation of these dependencies of children can be used to constrain the adult daily activity schedules. The deployment of this framework prior to the simulation of adult non-mandatory activities is expected to significantly enhance the representation of the interactions between children and adults in activity-based microsimulation models. / Dissertation/Thesis / M.S. Civil and Environmental Engineering 2010

This thesis examines the time-use patterns of adults in dual-earner households with and without children as a function of several individual and household socio-demographics and employment characteristics. A disaggregate activity purpose classification including both in-home and out-of-home activity pursuits is used because of the travel demand relevance of out-of-home pursuits, as well as to examine both mobility-related and general time-use related social exclusion and time poverty issues. The study uses the Nested Multiple Discrete Continuous Extreme Value (MDCNEV) model, which recognizes that time-decisions entail the choice of participating in one or more activity purposes along with the amount of time to invest in each chosen activity purpose, and allows generic correlation structures to account for common unobserved factors that might impact the choice of multiple alternatives. The 2010 American Time Use Survey (ATUS) data is used for the empirical analysis. A major finding of the study is that the presence of a child in dual-earner households not only leads to a reduction in in-home activity participation but also a substantially larger decrease in out-of-home activity participation, suggesting a higher level of mobility-related social exclusion relative to overall time-use social exclusion. To summarize, the results in the thesis underscore the importance of re-designing work policies in the United States to facilitate a reduction in work-family conflict in dual-earner families. / text

abstract: This dissertation research contributes to the advancement of activity-based travel forecasting models along two lines of inquiry. First, the dissertation aims to introduce a continuous-time representation of activity participation in tour-based model systems in practice. Activity-based travel demand forecasting model systems in practice today are largely tour-based model systems that simulate individual daily activity-travel patterns through the prediction of day-level and tour-level activity agendas. These tour level activity-based models adopt a discrete time representation of activities and sequence the activities within tours using rule-based heuristics. An alternate stream of activity-based model systems mostly confined to the research arena are activity scheduling systems that adopt an evolutionary continuous-time approach to model activity participation subject to time-space prism constraints. In this research, a tour characterization framework capable of simulating and sequencing activities in tours along the continuous time dimension is developed and implemented using readily available travel survey data. The proposed framework includes components for modeling the multitude of secondary activities (stops) undertaken as part of the tour, the time allocated to various activities in a tour, and the sequence in which the activities are pursued. Second, the dissertation focuses on the implementation of a vehicle fleet composition model component that can be used not only to simulate the mix of vehicle types owned by households but also to identify the specific vehicle that will be used for a specific tour. Virtually all of the activity-based models in practice only model the choice of mode without due consideration of the type of vehicle used on a tour. In this research effort, a comprehensive vehicle fleet composition model system is developed and implemented. In addition, a primary driver allocation model and a tour-level vehicle type choice model are developed and estimated with a view to advancing the ability to track household vehicle usage through the course of a day within activity-based travel model systems. It is envisioned that these advances will enhance the fidelity of activity-based travel model systems in practice. / Dissertation/Thesis / Doctoral Dissertation Civil and Environmental Engineering 2014

The research in the field of travel demand modeling is driven by the need to understand individuals’ behavior in the context of travel-related decisions as accurately as possible. In this regard, the activity-based approach to modeling travel demand has received substantial attention in the past decade, both in the research arena as well as in practice. At the same time, recent efforts have been focused on more fully realizing the potential of activity-based models by explicitly recognizing the multi-dimensional nature of activity-travel decisions. However, as more behavioral elements/dimensions are added, the dimensionality of the model systems tends to explode, making the estimation of such models all but infeasible using traditional inference methods. As a result, analysts and practitioners often trade-off between recognizing attributes that will make a model behaviorally more representative (from a theoretical viewpoint) and being able to estimate/implement a model (from a practical viewpoint). An alternative approach to deal with the estimation complications arising from multi-dimensional choice situations is the technique of composite marginal likelihood (CML). This is an estimation technique that is gaining substantial attention in the statistics field, though there has been relatively little coverage of this method in transportation and other fields. The CML approach is a conceptually and pedagogically simpler simulation-free procedure (relative to traditional approaches that employ simulation techniques), and has the advantage of reproducibility of the results. Under the usual regularity assumptions, the CML estimator is consistent, unbiased, and asymptotically normally distributed. The discussion above indicates that the CML approach has the potential to contribute in the area of travel demand modeling in a significant way. For example, the approach can be used to develop conceptually and behaviorally more appealing models to examine individuals’ travel decisions in a joint framework. The overarching goal of the current research work is to demonstrate the applicability of the CML approach in the area of activity-travel demand modeling and to highlight the enhanced features of the choice models estimated using the CML approach. The goal of the dissertation is achieved in three steps as follows: (1) by evaluating the performance of the CML approach in multivariate situations, (2) by developing multidimensional choice models using the CML approach, and (3) by demonstrating applications of the multidimensional choice models developed in the current dissertation. / text