Abstract
Abstract. Many studies have utilized the spatial correlations among traffic crash data to develop crash prediction models with the aim to investigate the influential factors or predict crash counts at different sites. The spatial correlation have been observed to account for heterogeneity in different forms of weight matrices which improves the estimation performance of models. But very rarely have the weight matrices been compared for the prediction accuracy for estimation of crash counts. This study was targeted at the comparison of two different approaches for modelling the spatial correlations among crash data at macro-level (County). Multivariate Full Bayesian crash prediction models were developed using Decay-50 (distance-based) and Queen-1 (adjacency-based) weight matrices for simultaneous estimation crash counts of four different modes: vehicle, motorcycle, bike, and pedestrian. The goodness-of-fit and different criteria for accuracy at prediction of crash count reveled the superiority of Decay-50 over Queen-1. Decay-50 was essentially different from Queen-1 with the selection of neighbors and more robust spatial weight structure which rendered the flexibility to accommodate the spatially correlated crash data. The consistently better performance of Decay-50 at prediction accuracy further bolstered its superiority. Although the data collection efforts to gather centroid distance among counties for Decay-50 may appear to be a downside, but the model has a significant edge to fit the crash data without losing the simplicity of computation of estimated crash count.
Highlights
From the past few decades, many fields have utilized the power of spatial nature of data for understanding the influence of space on different factors (Best et al, 2001)
The understanding of spatial correlations among crash data may be explained by a simple example: the intersections on a roadway corridor are exposed to similar amount of vehicle traffic and roadway geometry which lends a spatial influence to the types of crashes occurring on the intersections of that corridor
This study developed crash prediction models for different modes of crashes occurring in the 58 counties of California during the year 2012
Summary
From the past few decades, many fields have utilized the power of spatial nature of data for understanding the influence of space on different factors (Best et al, 2001). The macro level analysis of crash data usually serves the purpose to understand the impact of demographic or socioeconomic changes within an area on the crash trends. This broader perspective is highly beneficial to the planners who design and propose policies to control different factors with the aim to reduce crashes (Abdel-Aty et al, 2013). The spatial models have been developed to identify usually hidden factors or improve the estimation performance of models These correlations help account for the spatial dependency, which often escapes from the explanatory variables. Two different spatial models are developed for the crash data of 58 counties and the results are compared to assess superiority from different perspectives
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