Network Analysis of Road Crash Frequency using Spatial Models

Open Access
Aguero Valverde, Jonathan
Graduate Program:
Civil Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
June 24, 2008
Committee Members:
  • Paul Peter Jovanis, Committee Chair
  • Eric Todd Donnell, Committee Member
  • Murali Haran, Committee Member
  • Venkataraman Shankar, Committee Member
  • Peggy Ann Johnson, Committee Member
  • bayesian hierarchical models
  • spatial correlation
  • highway safety
  • Poisson log-Normal conditional autoregressive
Despite the evident spatial character of road crashes, limited research has been conducted in road safety analysis to account for spatial correlation; further, the practical consequences of this omission are largely unknown. The purpose of this research is to explore the effect of spatial correlation in models of road crash frequency at the segment level. Different segment neighboring structures are tested to establish the most promising one in the context of modeling crash frequency in road networks. A Full Bayes hierarchical approach is used with conditional autoregressive effects for the spatial correlation terms. Analysis of crash, traffic and roadway inventory data from rural engineering districts in Pennsylvania and Washington indicate the importance of including spatial correlation in road crash models. Six different road classes were analyzed in a single network model to facilitate the inclusion of the spatial correlation structures in the whole state-maintained network simultaneously rather than in separate models by road type. The inclusion of spatial correlation has an important impact on the estimation of crash frequency models by explaining additional extra-Poisson variability present in the data ; hence, producing better fitting models and improving the precision of the crash frequency and excess crash frequency estimates compared with models with heterogeneity-only random effects. Pure distance-based neighboring models (i.e. exponential decay) performed poorly in comparison to adjacency-based or distance-order models. The results also suggest that spatial correlation is more important in distances of one mile or less. The inclusion of spatially correlated random effects significantly improves the precision of the estimates of the expected crash frequency for each segment by ‘pooling’ strength from their neighbors; thus, reducing their standard deviation. This is a significant advantage of spatial models since poor estimates due to small sample sizes and low sample means is a frequent issue in highway safety analysis.