A multi-scale road segmentation approach to assess MAUP effects on crash frequency and hotspot analysis

Road traffic crashes remain a major global safety concern, and segment-level crash analysis plays a critical role in identifying high-risk locations. However, such analyses are highly sensitive to spatial unit definitions, giving rise to the Modifiable Areal Unit Problem (MAUP). Existing studies predominantly rely on fixed or single-scale segmentation, which limits their ability to capture scale-dependent effects and may bias both model estimation and hotspot identification. To address this issue, this study proposes a hybrid multi-scale road segmentation framework that integrates roadway homogeneity with crash distribution characteristics. The framework employs a Poisson likelihood ratio test and leave-one-out cross-validation (LOOCV) to generate adaptive segmentation schemes, and evaluates MAUP effects through crash distribution analysis, negative binomial modeling, and external validation. The results show that segmentation choice materially affects statistical representation, model estimation, and predictive performance. Both the scale effect and the zoning effect of MAUP are found to influence crash modeling and hotspot-related inference, although their impacts are not identical. External validation reveals substantial performance differences among segmentation schemes, with Seg-6 showing the strongest predictive performance within the original parameter set; the sensitivity analysis further indicates that this result is locally robust within the evaluated parameter neighborhood. Major crash concentration patterns remain broadly stable across segmentation schemes, whereas minor local variations are more segmentation-sensitive. These findings show that segmentation should be treated as an explicit analytical design issue rather than a neutral preprocessing step, and provide a systematic basis for evaluating MAUP effects in segment-level traffic safety analysis.