The development of modeling systems for activity-based travel demand ushers in a new era in transportation demand forecasting and planning. A comprehensive multimodal activity-based system for forecasting travel demand was developed for implementation in Florida and resulted in the Florida Activity Mobility Simulator (FAMOS). Two main modules compose the FAMOS microsimulation model system for modeling activity-travel patterns of individuals: the Household Attributes Generation System and the Prism-Constrained Activity-Travel Simulator. FAMOS was developed and estimated with household activity and travel data collected in southeast Florida in 2000. Results of the model development effort are promising and demonstrate the applicability of activity-based model systems in travel demand forecasting. An overview of the model system, a description of its features and capabilities, and preliminary validation results are provided.

Florida activity mobility simulator - Overview and preliminary validation results

Cities around the world are trying out a multitude of transportation policy and investment alternatives with the aim of reducing car-induced externalities. However, without a solid understanding of how people make their transportation and residential location choices, it is hard to tell which of these policies and investments are really doing the job and which are wasting precious city resources. The focus of this paper is the determinants of car ownership and car use for commuting. Using survey data from 1997 to 1998 collected in New York City, this paper uses discrete choice econometrics to estimate a model of the choices of car ownership and commute mode while also modeling the related choice of residential location. The main story told by this analysis is that New Yorkers are more sensitive to changes in travel time than they are to changes in travel cost. The model predicts that the most effective ways to reduce both auto ownership and car commuting involve changing the relative travel times for cars and transit, making transit trips faster by increasing both the frequency and the speed of service and making auto trips slower – perhaps simply by allowing traffic congestion. Population density also appears to have a substantial effect on car ownership in New York.

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Neighborhoods, cars, and commuting in New York City: A discrete choice approach

FAMOS: The Florida activity mobility simulator

Author(s): Salon, Deborah | Abstract: This dissertation is an exploration of the relationship between the transportation-land use system in New York City and the transportation and residential location choices made by New Yorkers The focus is on understanding these location and travel choices made by urbanites Specifically, this research uses discrete choice models to identify and quantify the effects of the variables that factor into New Yorkers' decisions about where to live, whether to own a car, and how to get around in their daily lives These models, along with GIS technology, are used to answer the following questions:1 How far off are the results of models that do not take all three of these decisions as endogenous?2 In a densely populated urban environment, what are the policy-sensitive factors that determine whether households own cars and how often walking is the mode of choice?3 How does the relative importance of these factors change across different neighborhoods within the city?4 How much of the relationship between land use patterns and travel behavior is due to the indirect effects of neighborhood and car ownership choice, and how large is the direct effect of land use patterns on travel behavior?Results indicate that the choices of residential location and commute mode are closely related; models of only commute mode choice produce biased results Models that do not take the choice of car ownership as endogenous in New York do not appear to be severely biasedFull models of the three choices indicate that the most important policy- sensitive factors influencing car ownership and mode choice are commute cost, commute time, and population density A set of spatial scenario analyses illustrate that the importance of these factors does indeed vary across neighborhoods within the cityFinally, a methodology is developed to separate the direct effect of land use patterns on travel behavior from the indirect effects The example used here identifies the direct and indirect effects of population density on the propensity to walk, finding that approximately half of the total effect is direct

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Cars and the City: An Investigation of Transportation and Residential Location Choices in New York City

A set of stop frequency models used to predict the number of nonwork activity stops made by individual adult workers on their way to work (home-based morning commute), their way home from work (home-based evening commute), and during their working hours (work-based midday travel) is presented. The ordered probit formulation was used to account for the ordered classification scale of the dependent variable (in this case, number of stops—zero stop, one stop, and two or more stops). With the New York Metropolitan Transportation Council household interview data, the models were used to examine commute stop-making behavior for individual workers residing in New York City (including the boroughs of Manhattan, Brooklyn, Queens, Bronx, and Staten Island). The empirical results provide useful insights into the effects of sociodemographics, land use density, transportation service, and work schedule on commute stop-making propensity in a highly urbanized environment. The findings also provide exploratory methodolog...

Empirical Analysis of Commute Stop-Making Behavior

This paper presents a nested logit model of activity scheduling behavior that can be used to predict a daily activity pattern for commuters. The behavioral paradigm embodied in the model suggests a two-stage decision process in which commuters first plan or identify the nonwork activities that need to be undertaken during the day, and second, schedule these activities in relation to the work activity schedule. Three possible scheduling periods are considered in the model: before work, at work, and after work. Alternative nested logit model structures are estimated on the 1996 San Francisco Bay Area activity survey sample to identify a plausible and statistically acceptable structure. Numerical examples are presented to show how the model, when combined with a Monte Carlo simulation and simple heuristics, can be used to generate daily activity schedule for commuters.

R G McCullough

Papers

A nested logit model of commuters' activity schedules