Wang X.C.,Rensselaer Polytechnic Institute |
Kockelman K.M.,University of Texas at Austin |
Lemp J.D.,Cambridge Systematics
Journal of Transport Geography | Year: 2012
Many transportation-related behaviors involve multinomial discrete response in a temporal and spatial context. These include quality of paved roadway sections over time, evolution of land use at the parcel level, vehicle purchases by socially networked households, and mode choices by individuals residing across adjacent homes or neighborhoods. Such responses depend on various influential factors, and can have temporal and spatial dependence or autocorrelation. In many cases, dynamic spatial-model specifications based on maximum fitness, profit or utility may be most appropriate.This study develops a dynamic spatial multinomial probit (DSMNP) model by pivoting off the ordinary MNP model while incorporating spatial and temporal dependencies. The study adds value to existing work by addressing polytomous outcomes and space-time data. (Most spatial models rely on cross-sectional data sets and/or binary outcomes.) The paper first explains how the model reflects behaviors at play, and then describes estimation using Bayesian methods, which are of great interest in multiple fields. Simulated data sets containing both generic and alternative-specific explanatory variables are used to validate the model's performance (and that of its associated code). Estimation efficiency issues and identification issues are discussed. The model is then applied to analyze parcel-level land use changes in Austin, Texas. It is found that better accessibility boosts the potential of residential development while hampering non-residential development. The effects of job and population density, neighborhood income and soil slope are also explored, and found to exert variable effects across space. It is also found that land development tends to cluster when existing development intensity in a neighborhood is low. © 2012 Elsevier Ltd.
Lemp J.D.,Cambridge Systematics |
Kockelman K.M.,University of Texas at Austin |
Unnikrishnan A.,West Virginia University
Accident Analysis and Prevention | Year: 2011
Long-combination vehicles (LCVs) have significant potential to increase economic productivity for shippers and carriers by decreasing the number of truck trips, thus reducing costs. However, size and weight regulations, triggered by safety concerns and, in some cases, infrastructure investment concerns, have prevented large-scale adoption of such vehicles. Information on actual crash performance is needed. To this end, this work uses standard and heteroskedastic ordered probit models, along with the United States' Large Truck Crash Causation Study, General Estimates System, and Vehicle Inventory and Use Survey data sets, to study the impact of vehicle, occupant, driver, and environmental characteristics on injury outcomes for those involved in crashes with heavy-duty trucks. Results suggest that the likelihood of fatalities and severe injury is estimated to rise with the number of trailers, but fall with the truck length and gross vehicle weight rating (GVWR). While findings suggest that fatality likelihood for two-trailer LCVs is higher than that of single-trailer non-LCVs and other trucks, controlling for exposure risk suggest that total crash costs of LCVs are lower (per vehicle-mile traveled) than those of other trucks. © 2010 Elsevier Ltd.
Lemp J.D.,Cambridge Systematics |
Kockelman K.M.,University of Texas at Austin
Transportation Research Record | Year: 2010
Numerous models of travel timing have been calibrated and reported in the literature. Some studies have treated time as a discrete variable by using familiar discrete choice methods, whereas others have treated time in a continuous fashion. Both approaches offer distinct advantages. Here a continuous logit model of work tour departure time choice is estimated; this model offers the advantage of a continuous-time response. A random utility maximization structure is used to capitalize on the key advantages of both main approaches to the modeling of travel timing. Bayesian techniques are used to estimate model parameters, and estimation results suggest a variety of predictive densities for departure times across different individuals. In addition, ordinary least squares regression models are used to estimate travel times and their variance across times of day for the auto and transit modes. These network variables are used to inform estimation of the continuous logit model of departure time. The results are meaningful for multiple applications, and the continuous logit can readily be extended to a two-dimensional choice construct, such that the departure and return times can be modeled simultaneously. In addition, Bayesian estimation techniques allow for the utility function to take any number of forms, which may offer greater predictive ability.
Zorn L.,San Francisco County Transportation Authority |
Sall E.,San Francisco County Transportation Authority |
Wu D.,Cambridge Systematics
Transportation | Year: 2012
Information produced by travel demand models plays a large role decision making in many metropolitan areas, and San Francisco is no exception. Being a transit first city, one of the most common uses for San Francisco's travel model SF-CHAMP is to analyze transit demand under various circumstances. SF-CHAMP v 4.1 (Harold) is able to capture the effects of several aspects of transit provision including headways, stop placement, and travel time. However, unlike how auto level of service in a user equilibrium traffic assignment is responsive to roadway capacity, SF-CHAMP Harold is unable to capture any benefit related to capacity expansion, crowding's effect on travel time nor or any of the real-life true capacity limitations. The failure to represent these elements of transit travel has led to significant discrepancies between model estimates and actual ridership. Additionally it does not allow decision-makers to test the effects of policies or investments that increase the capacity of a given transit service. This paper presents the framework adopted into a more recent version of SF-CHAMP (Fury) to represent transit capacity and crowding within the constraints of our current modeling software. © 2012 Springer Science+Business Media, LLC.
Deutsch-Burgner K.,University of California at Santa Barbara |
Ravualaparthy S.,Cambridge Systematics |
Goulias K.,University of California at Santa Barbara
Transportation | Year: 2014
The way in which a person organizes his or her day, both temporally and spatially, is a highly important matter to travel behavior and travel demand modeling. Many times, the focus of these models is to accurately predict the “where” and “when”, without paying adequate attention to the “why.” The participation in activities, and therefore the selection of a place for these activities has been recently discussed within the framework of subjective well being. The motivation of happiness can be used to understand how and why people make the choices that they do. Many different criteria are used by individuals in the selection of destinations. These criteria range from attributes such as distance and cost, to attributes such as comfort, security and social aspects in determining the most rewarding destinations. Aspects contributing to a rewarding experience can also be viewed as those decision criteria that lead to the highest satisfaction. In this paper, several attributes of places and decision-making are explored for their potential to explain destination choices. First, a broader analysis of destination choice and criteria used helps us develop a geographic representation of attitudes and views regarding the area of Santa Barbara, California. Following this general evaluation of space, individual activity types are statistically analyzed in the importance different attributes play in the selection of a destination that leads to higher satisfaction. © 2014, Springer Science+Business Media New York.