Grouped random parameters bivariate probit analysis of perceived and observed aggressive driving behavior: A driving simulation study

Sarwar, Tawfiq; Anastasopoulos, Panagiotis Ch.; Golshani, Nima; Hulme, Kevin F.

doi:10.1016/j.amar.2016.12.001

Cited by 75 publications

(65 citation statements)

References 47 publications

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“…Compared to fixed parameters models assuming the same effects of factors on all observations, random parameters models can capture the observation-specific effects of factors on crash frequency and have also been widely applied in crash injury severity analyses (Russo et al, 2014;Zhao and Khattak, 2015, Behnood and Mannering, 2016, 2017a, 2017bNaik et al, 2016;Anderson and Hernandez, 2017;Fountas and Anastasopoulos, 2017;Seraneeprakarn et al, 2017) and crash rate analyses (Anastasopoulos, 2016). Especially, for the data where one entity has multiple observations, such as panel data, group-specific random parameters models may be adopted to account for heterogeneity among groups (Wu et al, 2013;Sarwar et al, 2017). More details about random parameters formulations can be seen in the study by Mannering et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes

Liu

Zhao

et al. 2018

Analytic Methods in Accident Research

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Urban midblock crashes are influenced mainly by traffic operation and roadway geometric features. In this paper, 10-year crash data from 1,506 directional urban midblock segments in Nebraska were analyzed using the multivariate random parameters zero-inflated negative binomial model to account for unobserved heterogeneity produced by correlations across segments, correlations across crash collision types, excessive zero crashes, and over dispersion. The multivariate random parameters zero-inflated negative binomial model was superior to many common crash frequency models in terms of both goodness of fit and prediction accuracy. Compared with the multivariate fixed parameters zero-inflated negative binomial model, the multivariate random parameters zero-inflated negative binomial model identified fewer key influencing factors and revealed segment-specific effects of these factors on different crash types. It showed that the number of lanes, annual average daily traffic per lane, and segment length might have non-positive effects on crash frequencies. Segments with a speed limit of 45 mph had fewer crashes than did those with lower speed limits, and there were fewer crashes on the segments in Omaha than on those in Lincoln. It was also found that neither the presence of a shoulder, on-street parking, or one-way traffic, nor lane width had significant influences on crash frequencies. These findings are informative for transportation agencies to take correct and efficient measures to accommodate diverse transportation demands without reducing traffic safety. By contrast, the fixed parameters model produced results consistent with intuition, but the results were insufficient to provide actionable recommendations.

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Section: Introductionmentioning

confidence: 99%

Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes

Liu

Zhao

et al. 2018

Analytic Methods in Accident Research

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“…This is an important consideration, as if unobserved heterogeneity is not addressed, the parameter estimates can be erroneous, the inferences inconsistent, and the forecasts inaccurate (Washington et al, 2011;Russo et al, 2014;Anastasopoulos and Mannering, 2016;Mannering et al, 2016;Sarwar et al, 2016Sarwar et al, , 2017. In random parameters modeling, the coefficients become:…”

Section: Methods and Approachmentioning

confidence: 99%

Project Discrepancies in Roadway Construction and Preservation: A Statistical Analysis of Public–Private Partnership Contract Types in the US

Nahidi

Fountas

Sarvani

et al. 2017

Front. Built Environ.

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At the planning stage of a roadway construction or preservation project, public agencies face a crucial challenge; to adopt and implement the appropriate contractual framework that can minimize the anticipated project discrepancies, on the basis of project-specific conditions. Focusing on public-private partnerships (PPPs), a deeper understanding of the factors that affect each type of discrepancy can assist public agencies in thoroughly evaluating and comparing PPP options. This paper seeks to provide an empirical but comprehensive analysis on PPP-specific project discrepancies, by identifying influential factors that significantly affect cost savings, cost overrun, change orders, and time delays in roadway projects. Using data from 645 PPP projects implemented in the USA between 1996 and 2011, seven popular PPP contracting approaches in the USA were considered in the analysis, and a number of random parameters linear and non-linear statistical models were estimated by PPP approach. The results demonstrate that PPP-specific attributes such as project cost, size, types of constituent activities, and expected duration have mixed effects on the occurrence and the magnitude of each type of discrepancy.

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“…A number of traffic safety studies accounted for group effects (instead of site effects) in random effects, random parameters, and latent class modeling to capture variations in unknown/unmeasured factors that vary across groups (Wu et al, 2013;Heydari et al, 2014a;Heydari et al, 2016b;Sarwar et al, 2017;Fountas et al, 2018a and2018b;Cai et al, 2018). A discussion on the importance of accounting for group (e.g., region) effects in traffic safety research is provided in Heydari et al, 2016b.…”

Section: Unobserved Heterogeneitymentioning

confidence: 99%

Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: Applications to grade crossing safety analysis

Heydari

Thakali

et al. 2018

Analytic Methods in Accident Research

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Comparing regions while adjusting for differences in characteristics of sites located in those regions is valuable since it identifies possible interregional dissimilarities in crash risk propensities according to specific safety performance measures (e.g., crash frequency of a specific type). This paper describes a framework to benchmark different regions (neighborhoods, provinces, etc.) in terms of a selected safety performance measure. To avoid issues relating to aggregated (macro-level) data, we use disaggregate (micro-level) data to draw inferences at a macro/region-level, which is often needed for developing large-scale transportation safety and planning programs and policies. To overcome unobserved heterogeneity, we employ a multilevel Bayesian heteroskedastic Poisson lognormal model with grouped random parameters allowing heterogeneity in both mean and variance parameters. The proposed approach is illustrated through a comprehensive study of highway railway grade crossings across Canada. The results indicate that the proposed model addresses unobserved heterogeneity more efficiently and provides more insight compared to conventional random parameters models. For example, we found that as traffic exposure increases, grade crossing safety deteriorates at a higher rate in the Canadian Prairies than in the other regions. Our benchmarking framework is also affected by different model specifications. The results indicate the need for further indepth investigations, which could help to identify possible reasons for inter-region differences in terms of specific safety indicators. This study provides valuable guidelines to Canadian transportation authorities, revealing important underlying crash mechanisms at highway railway grade crossings in Canada.

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Grouped random parameters bivariate probit analysis of perceived and observed aggressive driving behavior: A driving simulation study

Cited by 75 publications

References 47 publications

Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes

Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes

Project Discrepancies in Roadway Construction and Preservation: A Statistical Analysis of Public–Private Partnership Contract Types in the US

Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: Applications to grade crossing safety analysis

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