Abstract
Many neighborhood weight matrices have been adopted for modeling crash spatial heterogeneity. However, there has been little evaluation of their influence on crash prediction modeling performance. This study investigated 17 spatial-proximity matrices for development of spatial crash prediction models and site ranking with county-level data in California. Of the group of matrices being evaluated, traffic exposure–weighted and population-weighted distance-based matrices were first proposed in the traffic safety field. Bayesian spatial analysis was conducted with a combination of a first-order autoregressive error process and time trend for crashes to address the serial correlation of crashes in successive years. Two diagnostic measures were used for assessment of goodness of fit and complexity of models, and seven evaluation criteria were employed to assess the benefits associated with better-fitting models in site ranking. The results showed that modeling performance improved with an increase in number of neighbors considered in the weight matrix. However, a larger number of neighbors also led to greater variability of modeling performance. Specifically, Queen-2 and Decay-50 models proved to be superior among the adjacency- and distance-based models, respectively. The significance of incorporating spatial correlations was highlighted by the consistently poor performance of the base model, which included only the heterogeneity random effect. Finally, model-fitting performance seems to be strongly correlated with site-ranking performance. The models with closer goodness of fit tend to yield more consistent ranking results.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Transportation Research Record: Journal of the Transportation Research Board
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.