Sensitivity analysis in the context of regional safety modeling: Identifying and assessing the modifiable areal unit problem

•Conducting a sensitivity analysis to quantitatively investigate the modifiable areal unit problem in the context of regional safety modeling.•The Bayesian spatial model outperforms the Bayesian Poisson-lognormal model in accurately accounting for spatial autocorrelation effects, unbiased parameter estimates, and model performance.•Zoning schemes with higher number of zones tend to have increasing number of significant variables, more stable coefficient estimation, smaller standard error, whereas worse model performance.•The variables of population density and median household income show consistently significant effects on crash risk and are robust to variation in data aggregation.•The MAUP effects may be significantly reduced if we just maintain at about 50% of the original number of zones.

A wide array of spatial units has been explored in current regional safety analysis. Since traffic crashes exhibit extreme spatiotemporal heterogeneity which has rarely been a consideration in partitioning these zoning systems, research based on these areal units may be subjected to the modifiable areal unit problem (MAUP).This study attempted to conduct a sensitivity analysis to quantitatively investigate the MAUP effect in the context of regional safety modeling. The emerging regionalization method-RECDAP (regionalization with dynamically constrained agglomerative clustering and partitioning) was employed to aggregate 738 traffic analysis zones in the county of Hillsborough to 14 zoning schemes at an incremental step-size of 50 zones based on spatial homogeneity of crash risk. At each level of aggregation, a Bayesian Poisson lognormal model and a Bayesian spatial model were calibrated to explain observed variations in total/severe crash counts given a number of zone-level factors.Results revealed that as the number of zones increases, the spatial autocorrelation of crash data increases. The Bayesian spatial model outperforms the Bayesian Poisson-lognormal model in accurately accounting for spatial autocorrelation effects, unbiased parameter estimates, and model performance, especially in cases with higher disaggregated levels. Zoning schemes with higher number of zones tend to have increasing number of significant variables, more stable coefficient estimation, smaller standard error, whereas worse model performance. The variables of population density and median household income show consistently significant effects on crash risk and are robust to variation in data aggregation. The MAUP effects may be significantly reduced if we just maintain at about 50% of the original number of zones (350 or larger). The present study highlights MAUP that is generally ignored by transportation safety analysts, and provides insights into the nature of parameter sensitivity to data aggregation in the context of regional safety modeling.