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College Station, TX, United States

The Texas A&M Transportation Institute in College Station, Texas is the largest transportation research agency in the United States. Created in 1950, primarily in response to the needs of the Texas Highway Department , TTI has since broadened its focus to address all modes of transportation–highway, air, water, rail and pipeline Wikipedia.

Cheng L.,Texas A&M University | Geedipally S.R.,Texas Transportation Institute | Lord D.,Texas A&M University
Safety Science

Over the last 20-30. years, there has been a significant amount of tools and statistical methods that have been proposed for analyzing crash data. Yet, the Poisson-gamma (PG) is still the most commonly used and widely acceptable model. This paper documents the application of the Poisson-Weibull (PW) generalized linear model (GLM) for modeling motor vehicle crashes. The objectives of this study were to evaluate the application of the PW GLM for analyzing this kind of dataset and compare the results with the traditional PG model. To accomplish the objectives of the study, the modeling performance of the PW model was first examined using a simulated dataset and then several PW and PG GLMs were developed and compared using two observed crash datasets. The results of this study show that the PW GLM performs as well as the PG GLM in terms of goodness-of-fit statistics. © 2012 Elsevier Ltd. Source

Moore D.N.,University of Akron | Schneider IV W.H.,University of Akron | Savolainen P.T.,Wayne State University | Farzaneh M.,Texas Transportation Institute
Accident Analysis and Prevention

Standard multinomial logit (MNL) and mixed logit (MXL) models are developed to estimate the degree of influence that bicyclist, driver, motor vehicle, geometric, environmental, and crash type characteristics have on bicyclist injury severity, classified as property damage only, possible, nonincapacitating or severe (i.e.; incapacitating or fatal) injury. This study is based on 10,029 bicycleinvolved crashes that occurred in the State of Ohio from 2002 to 2008. Results of likelihood ratio tests reveal that some of the factors affecting bicyclist injury severity at intersection and non-intersection locations are substantively different and using a common model to jointly estimate impacts on severity at both types of locations may result in biased or inconsistent estimates. Consequently, separate models are developed to independently assess the impacts of various factors on the degree of bicyclist injury severity resulting from crashes at intersection and non-intersection locations. Several covariates are found to have similar impacts on injury severity at both intersection and non-intersection locations. Conversely, six variables were found to significantly influence injury severity at intersection locations but not non-intersection locations while four variables influenced bicyclist injury severity only at non-intersection locations. In crashes occurring at intersection locations, the likelihood of severe bicyclist injury increases by 14.8 percent if the bicyclist is not wearing a helmet, 82.2 percent if the motorist is under the influence of alcohol, 141.3 percent if the crash-involved motor vehicle is a van, 40.6 percent if the motor vehicle strikes the side of the bicycle, and 182.6 percent if the crash occurs on a horizontal curve with a grade. Results from non-intersection locations show the likelihood of severe injuries increases by 374.5 percent if the bicyclist is under the influence of drugs, 150.1 percent if the motorist is under the influence of alcohol, 53.5 percent if the motor vehicle strikes the side of the bicycle and 99.9 percent if the crash-involved motor vehicle is a heavy-duty truck. © 2010 Elsevier Ltd All rights reserved. Source

Bricka S.G.,Texas Transportation Institute | Sen S.,NuStats | Paleti R.,University of Texas at Austin | Bhat C.R.,University of Texas at Austin
Transportation Research Part C: Emerging Technologies

Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics. © 2011 Elsevier Ltd. Source

Devarasetty P.C.,Texas A&M University | Zhang Y.,Texas A&M University | Fitzpatrick K.,Texas Transportation Institute
Journal of Transportation Engineering

Left-turn gap acceptance at an unsignalized intersection is dependent on many factors. The Highway Capacity Manual (HCM) uses a single value of critical gap for all types of intersections; however, this may be oversimplistic and lead to inaccurate estimates of left-turn delay and capacity. Most existing studies also do not differentiate between gap and lag when evaluating gap acceptance. In this paper, binary logit models were developed to estimate the probability of accepting or rejecting a given gap or lag for a left-turning vehicle from a major road at an unsignalized intersection considering a number of potential influencing factors. Gap acceptance behavior was found to be influenced by the type of gap presented to the driver (gap or lag). Gap duration, total wait time, time to turn, distance to next signal downstream, and median type were found to be significant factors in predicting the probability of accepting or rejecting a gap. In the model for lag acceptance lag duration, time to turn, crossing width, speed limit, and distance to next signal downstream were found to be significant. Equations for estimating the critical gap and lag were developed. Critical gap and lag were found to be varying over a wide range of values depending on the type of intersection. The range was smaller for critical gaps than lags. The findings from this study can improve operational analysis of left turns at unsignalized intersections by using different critical gaps for different traffic and geometric conditions. © 2012 American Society of Civil Engineers. Source

Geedipally S.,Texas Transportation Institute | Lord D.,Texas A&M University
Transportation Research Record

The Poisson-gamma (negative binomial or NB) distribution is still the most common probabilistic distribution used by transportation safety analysts to model motor vehicle crashes. Recent studies have shown that the Conway-Maxwell-Poisson (COM-Poisson) distribution also is promising for developing crash prediction models. The objectives of this study were to investigate and compare the estimation of crash variance predicted by the COM-Poisson generalized linear model (GLM) and the traditional NB model. The comparison analysis was carried out with the most commonly employed functional forms, which linked crashes to the entering flows and other explanatory variables at intersections or on segments. To accomplish the objectives of the study, several NB and COM-Poisson GLMs (including flow-only models and models with several covariates) were developed and compared by using two data sets. The first data set contained crash data collected at signalized, four-legged intersections in Toronto, Ontario, Canada. The second data set included data collected on rural, four-lane, undivided highways in Texas. The results of this study show that the trend of crash variance prediction by COM-Poisson GLM is similar to that predicted by the NB model. The Spearman's rank correlation coefficients between the crash variance predicted by the COM-Poisson and the NB model confirmed that there was a perfect monotone increasing, and the values were highly correlated. This correlation means that a site characterized by a large variance would essentially be identified as such, whether the NB model or the COM-Poisson model was used. Source

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