Austin, TX, United States
Austin, TX, United States

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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.


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.


Furth P.G.,Northeastern University | Cesme B.,Northeastern University | Rima T.,Cambridge Systematics
Transportation Research Record | Year: 2010

Near major bus terminals, multiple bus arrivals per signal cycle and a convergence of buses from conflicting directions can make it impractical to apply signal priority logic that attempts to interrupt the signal cycle for each bus. This research explores signal control logic for reducing bus delay around a major bus terminal in Boston, Massachusetts, where the busiest intersections see almost four buses per signal cycle. With a traffic microsimulation to model a succession of signal priority tactics, a reduction in bus delay of 22 s per intersection was obtained, with no significant impact on general traffic. The general strategy was to provide buses with green waves, so that they are stopped at most once, coupled with strategies to minimize initial delay. The greatest delay reduction came from passive priority treatments: changing phase sequence, splits, and offsets to favor bus movements. Green extension and green insertion were found to be effective for reducing initial delay and for providing dynamic coordination. Dynamic phase rotation, from lagging to leading left, proved less effective. Cycle-constrained free actuation, in which an intersection has a fixed cycle length within which two phases can alternate freely, provided flexibility for effective application of early green and green extension at one intersection with excess capacity. Emphasis is given to the approach of providing aggressive priority with compensation for interrupted phases, highlighting the compensation mechanism afforded by actuated control with snappy settings and long maximum greens.


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.


Paul B.M.,University of Texas at Austin | Kockelman K.M.,University of Texas at Austin | Musti S.,Cambridge Systematics
Transportation Research Record | Year: 2011

With environmental degradation and energy security as serious concerns, it is important to anticipate how vehicle ownership and usage patterns can change under different policies and contexts. This work ascertains the acquisition, disposal, and use patterns of personal vehicles of a synthetic population over time and relies on microsimulation to anticipate fleet composition, usage, and greenhouse gas emissions under different settings. Twenty-five-year simulations predict the highest market share for plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles, and smart cars and the greatest reductions in carbon emissions under an increased gasoline price ($7/gal). Results under a "feebate" policy scenario (where fees apply to low-fuel-economy vehicles, and rebates rise with fuel economies above a threshold) indicated a shift toward fuel-efficient vehicles but with vehicle miles traveled (VMT) rising, thanks to lower driving costs. Excepting the low PHEV price and feebate policy simulations, all other scenarios predicted a lower fleet VMT value. The high-density scenario (job and household densities quadrupled) resulted in the lowest vehicle ownership levels and lower VMT values and emissions. The low-PHEV-price scenario resulted in higher shares of PHEVs but negligible impacts from greenhouse gas emissions. Adoption and widespread use of plug-in vehicles will depend on marketing, competitive pricing, government incentives, reliable driving-range reports, and charging infrastructure. Though just 29% of survey respondents stated support for a (specific) feebate policy, 35% indicated an interest in purchasing a PHEV if it cost just $6,000 more than its gasoline counterpart.


Greenberg A.,U.S. Federal Highway Administration | Evans J.J.,Cambridge Systematics
Transportation Research Record | Year: 2015

Converting fixed driving costs to variable per mile charges-and offering cash savings in lieu of parking for bundled or otherwise free parking- encourages voluntary curtailment of driving and related decreases in greenhouse gas (GHG) emissions, air pollution, congestion, and crashes. This research explores a potential regulatory approach to help achieve goals for the reduction of GHGs by setting transportation efficiency targets that are based on simultaneously deploying (a) pay-as-you-driveand- you-save car insurance, (b) parking cash-out, and (c) conversion of state and local sales taxes on newly purchased vehicles to mileage taxes designed to raise equivalent revenue. Through a best-estimate price elasticity of -0.30 (and the testing of others), a year 2030 comparison is made between projected state-level and national reductions in GHG emissions of the proposed transportation policy bundle and projected reductions from the U.S. Environmental Protection Agency (EPA) final rule for existing electric utility sources. The transportation policies would yield nationwide reductions in GHG emissions of 257 million metric tons (MMT) of carbon dioxide equivalent (CO2e) or 68.6% of those of the final electric utility rule (above those of that final rule, called the most significant U.S. government action ever for reducing GHG emissions) and would generate reductions greater than those calculated for the electric utility rule in 24 states plus the District of Columbia.


Musti S.,Cambridge Systematics | Kortum K.,University of Texas at Austin | Kockelman K.M.,University of Texas at Austin
Transportation Research Part D: Transport and Environment | Year: 2011

This study examines personal travel decisions and residents' opinions on energy policy options in the Austin metropolitan area. The vast majority of respondents recognized global warming as a problem, and most agreed that lifestyle changes are needed to combat climate change. Many also believe that climate change can be combated by application of stricter policies in the areas of vehicle technology, fuel economy, and building design. Results of the study illuminate the importance of home-zone attributes on vehicle ownership, vehicle miles, and emissions. Most households agree that energy regulations should be pursued to curb global climate change, and most prefer caps on consumption over taxation. The results suggest that substantial US energy and greenhouse gas savings are likely to come from vehicle fuel-economy regulation, rebates on relatively fuel-efficient vehicle purchases, caps on maximum household energy use, and long-term behavioral shifts. © 2010 Elsevier Ltd.


Kockelman K.M.,University of Texas at Austin | Lemp J.D.,Cambridge Systematics
Transportation Research Part A: Policy and Practice | Year: 2011

Pricing of roadways opens doors for infrastructure financing, and congestion pricing seeks to address inefficiencies in roadway operations. This paper emphasizes the revenue-generation opportunities and welfare impacts of flat-tolling schemes, standard congestion pricing, and credit-based congestion pricing policies. While most roadway investment decisions focus on travel time savings for existing trips, this work turns to logsum differences (which quantify changes in consumer surplus) for nested logit specifications across two traveler types, two destinations, three modes and three times of day, in order to arrive at welfare- and revenue-maximizing solutions. This behavioral specification is quite flexible, and facilitates benefit-cost calculations (as well as equity analysis), as demonstrated in this paper.The various cases examined suggest significant opportunities for financing new roadway investment while addressing congestion and equity issues, with net gains for both traveler types. Application results illustrate how, even after roadway construction and maintenance costs are covered, receipts may remain to distribute to eligible travelers so that typical travelers can be made better off than if a new, non-tolled road had been constructed. Moreover, tolling both routes (new and old) results in substantially shorter payback periods (5 versus 20. years) and higher welfare outcomes (in the case of welfare-maximizing tolls with credit distributions to all travelers). The tools and techniques highlighted here illustrate practical methods for identifying welfare-enhancing and cost-recovering investment opportunities, while recognizing multiple user classes and appropriate demand elasticity across times of day, destinations, modes and routes. © 2011 Elsevier Ltd.

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