Statistical and Econometric Methods for Transportation Data Analysis

By T. Coburn. Technometrics, 2004

1,568 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ee25d561db919a90284e71d9b2a3c99454dbf113

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Citations in the corpus

Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data (2018, methodology): “The marginal effect can be estimated as , where is the expected mean outcome in each modeling approach (Washington et al. 2010).”
Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression (2019, background): “As Washington, Karlaftis, and Mannering (2011) noted, models omitting spatial correlation underestimate the variance of parameters, which may lead to misidentification of the factors that contribute to crash occurrence.”
Alternative Modeling Approaches Used for Examining Automobile Ownership: A Comprehensive Review (2014, background): “Theoretically, SEM has two components, factor analysis/measurement model and structural equation/model (Aditjandra et al., 2012; Washington et al., 2011).”
Airport Security Screening and Changing Passenger Satisfaction: An Exploratory Assessment (2006, methodology): “Because this is an unnecessary and potentially erroneous restriction, we adopt an unordered discrete outcome model (see Washington et al., 2003, for a further explanation of this point).1 The most widely applied discrete-outcome modeling approach is the multinomial logit model.”
Model of Relationship between Interstate Crash Occurrence and Geometrics (2011, background): “(19), Ulfarsson and Shankar (20), and Sittikariya and Shankar (21) underscored the importance of heterogeneity as a result of time-varying effects.”
Short‐term traffic forecasting: Overview of objectives and methods (2004, background): “Both issues, concerning the statistical notion of accuracy (quantitative) and its meaning (qualitative), are discussed in the academic literature (Makridakis et al. , 1989; Chatfield, 1992, Clemens and Hendry, 1993, Washington et al. , 2003).”
Obesity relationships with community design, physical activity, and time spent in cars. (2004, neutral): “Recruitment based on household size and income was consistent with the state of the practice for self-reported household travel surveys, and subsequent analysis techniques that premised the sampling plan for this effort.(24) Phone numbers were obtained through a commercial reverse directory of liste”
Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. (2005, background): “As stated in Washington et al. (2003), “If there is not a compelling reason to suspect that two states might be present, the use of a zeroinflated model may be simply capturing model mis-specification that could result from factors such as unobserved effects (heterogeneity) in the data.””
The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. (2011, background): “parameters, and ε is a disturbance term (Washington et al., 2011).”
A joint econometric framework for modeling crash counts by severity (2018, result): “At the same time, ignoring the presence of such potential correlationmay result in biased parameter estimates and thus lead to inaccurate policy implications (Chamberlain 1980; Eluru and Bhat 2007; Washington, Karlaftis, and Mannering 2010).”
Speed Factors on Two-Lane Rural Highways in Free-Flow Conditions (2005, background): “It has to be stressed that strong correlation between explanatory variables (but not multicollinearity) increases the standard deviation of parameter estimates but does not prevent obtaining a best fit to the data, nor does it affect inferences on mean responses or new observations (19).”
The Gravity Model Specification for Modeling International Trade Flows and Free Trade Agreement Effects: A 10-Year Review of Empirical Studies (2010, methodology): “Moreover, the statistical techniques applied could result to more or less accurate model parameters [29].”
Investigation of factors influencing motorcyclist injury severity using random parameters logit model with heterogeneity in means and variances (2021, background): “For indicator variables, the marginal effects represent the impact on the probability of injury severity when an independent indicator variable changes value from zero to one [49].”