Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data

Buddhavarapu, Prasad; Scott, James G.; Prozzi, Jorge A

doi:10.1016/j.trb.2016.06.005

Cited by 52 publications

(20 citation statements)

References 43 publications

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“…The NB model as a special case of Poisson-Gamma mixture model is a variant of the Poisson model designed to deal with over-dispersed data (Lord and Mannering, 2010;Buddhavarapu et al, 2016). The over-dispersion could come from several possible sources, e.g., omitted variables, uncertainty in exposure data, covariates or nonhomogeneous LX environment (Miaou, 1994).…”

Section: Ar(x)mentioning

confidence: 99%

Developing accident prediction model for railway level crossings

et al. 2018

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Railway level crossing (LX) safety continues to be one of the most critical issues for railways, despite an everincreasing focus on improving design and application practices. Accidents at European LXs account for about one-third of the entire railway accidents and result in more than 300 deaths every year in Europe. Due to the non-deterministic causes, the complex operation background and the lack of thorough statistical analysis based on accident/incident data, the risk assessment of LXs remains a challenging task. In the present paper, some LX accident prediction models are developed. Such models allow for highlighting the influence of the main impacting parameters, i.e., the average daily road traffic, the average daily railway traffic, the annual road accidents, the vertical road profile, the horizontal road alignment, the road width, the crossing length, the railway speed limit and the geographic region. The Ordinary Least-Squares (OLS) and Nonlinear Least-Squares (NLS) methods are employed to estimate the respective coefficients of variables in the prediction models, based on the LX accident/incident data. The validation and comparison process is performed through statistical means to examine how well the estimation of the models fits the reality. The outcomes of validation and comparison attest that the improved accident prediction model has statisticbased approbatory quality. Moreover, the improved accident prediction model combined with the NB distribution shows relatively high predictive accuracy of the probability of accident occurrence.

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Section: Ar(x)mentioning

confidence: 99%

Developing accident prediction model for railway level crossings

et al. 2018

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“…The most common approach to consider full unobserved heterogeneities in crash likelihood modeling is a random-parameter model, which has been thoroughly investigated in various studies (Anastasopoulos and Mannering, 2009;Barua et al, 2016;Bhat et al, 2014;Chen and Tarko, 2014;Coruh et al, 2015;Venkataraman et al, 2011;Venkataraman et al, 2013). In addition, the latent-class model is another possible way to model unobserved effects in crash data (Buddhavarapu et al, 2016;Heydari et al, 2016), and random parameters can be further adopted within each class (Xiong and Mannering, 2013).…”

Section: Methodological Challenges In Crash Modelingmentioning

confidence: 99%

Gas dynamic analogous exposure approach to interaction intensity in multiple-vehicle crash analysis: Case study of crashes involving taxis

Meng

Wong

et al. 2017

Analytic Methods in Accident Research

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Exposure is a frequency measure of being in situations in which crashes could occur. In modeling multiple-vehicle crash frequency, traditional exposure measures, such as vehicle kilometrage and travel time, may not be sufficiently representative because they may include situations in which vehicles rarely meet each other and multiple-vehicle crashes can never happen. The meeting frequency of vehicles should be a better exposure measure in such cases. This study aims to propose a novel Gas Dynamic Analogous Exposure (GDAE) to model multiple-vehicle crash frequency. We analogize the meeting frequency of vehicles with the meeting frequency of gas molecules because both systems consider the numbers of the meetings of discrete entities. A meeting frequency function of vehicles is derived based on the central idea of the classical collision theory in physical chemistry with consideration of constrained vehicular movement by the road alignments. The GDAE is then formulated on

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“…Such fact has driven the development of statistical techniques to identify the unobserved heterogeneity. To accommodate the discrete nature of crash severity (no injury, slight injury, serious injury, and fatality), various regression approaches-random parameters logit (RP-logit) model [38,39], random parameters probit model [40], random intercept logit model [41], latent class logit model [10], and finite mixture random parameters model [16,42]-have been widely recommended due to their high flexibility [43][44][45]. Alternatively, random parameters ordered logit model [46] and random parameters ordered probit model [47] were applied to handle the intuitive ordering of crash severity.…”

Section: Statistical Techniques For Unobserved Heterogeneity and Spatial Correlationmentioning

confidence: 99%

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

et al. 2021

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Accident analysis and prevention are helpful to ensure the sustainable development of transportation. The aim of this research was to investigate the factors associated with the severity of low-visibility-related rural single-vehicle crashes. Firstly, a latent class clustering model was implemented to partition the whole-dataset into a relatively homogeneous sub-dataset. Then, a spatial random parameters logit model was established for each dataset to capture unobserved heterogeneity and spatial correlation. Analysis was conducted based on the crash data (2014–2019) from 110 two-lane road segments. The results show that the proposed method is a superior crash severity modeling approach to accommodate the unobserved heterogeneity and spatial correlation. Three variables—seatbelt not used, motorcycle, and collision with fixed object—have a stable positive correlation with crash severity. Motorcycle leads to a 12.8%, 23.8%, and 12.6% increase in the risk of serious crashes in the whole-dataset, cluster 3, and cluster 4, respectively. In the whole-dataset, cluster 2, and cluster 3, the risk of serious crashes caused by seatbelt not used increased by 5.5%, 0.1%, and 30.6%, respectively, and caused by collision with fixed object increased by 33.2%, 1.2%, and 13.2%, respectively. The results can provide valuable information for engineers and policy makers to develop targeted measures.

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Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data

Cited by 52 publications

References 43 publications

Developing accident prediction model for railway level crossings

Developing accident prediction model for railway level crossings

Gas dynamic analogous exposure approach to interaction intensity in multiple-vehicle crash analysis: Case study of crashes involving taxis

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

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