Multivariate Poisson-Lognormal Models for Jointly Modeling Crash Frequency by Severity
نویسندگان
چکیده
This paper introduces a new multivariate approach for jointly modeling crash counts by severity data based on Multivariate Poisson-Lognormal models. Although the crash frequency by severity data are multivariate in nature, they have often been analyzed by modeling each severity level separately without taking into account correlations that exist among different severity levels. The new Multivariate Poisson-Lognormal regression approach can cope with both overdispersion and a fully general correlation structure in the data as opposed to the recently suggested Multivariate Poisson regression approach that allows for neither over-dispersion nor a general correlation structure in the data. The new method is applied to the multivariate crash counts obtained from the intersections in California for 10 years. The results show promise towards the goal of obtaining more accurate estimates by accounting for correlations in the multivariate crash counts and over-dispersion.
منابع مشابه
Examining signalized intersection crash frequency using multivariate zero-inflated Poisson regression
In crash frequency studies, correlated multivariate data are often obtained for each roadway entity longitudinally. The multivariate models would be a potential useful method for analysis, since they can account for the correlation among the specific crash types. However, one issue that arises with this correlated multivariate data is the number of zero counts increases as crash counts have man...
متن کاملA multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods.
Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in p...
متن کاملA Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods.
This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-spec...
متن کاملRe-visiting crash-speed relationships: A new perspective in crash modelling.
Although speed is considered to be one of the main crash contributory factors, research findings are inconsistent. Independent of the robustness of their statistical approaches, crash frequency models typically employ crash data that are aggregated using spatial criteria (e.g., crash counts by link termed as a link-based approach). In this approach, the variability in crashes between links is e...
متن کاملPredicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model.
Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson-lognormal) for modelling the number of accidents at different severity levels simultaneously. This paper proposes an alternative method to...
متن کامل