# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 5 Targeted Safety Interventions
### Work Zone Safety and Roadway Infrastructure Factors  
[PERSON] and [PERSON] [81] analyzed crash frequency for different toll plaza designs. Compared with traditional plazas, hybrid designs cut crashes by 44.7% and all-electronic toll collection (AETC) by 72.6%. Diverge areas at hybrid plazas, however, exhibit 23% higher risk than merge areas.  
[PERSON] et al. [82] applied a random-parameter negative-binomial model with heterogeneity in means and variances to freeway ramp crashes. Ramp type, horizontal alignment, truck volume, and interchange geometry all significantly affect crash counts, demonstrating the need for spatially nuanced design standards.  
[PERSON] and [PERSON] [83] found that highway ice warning signs are not, by themselves, a significant crash-reduction factor--although their effects intertwine with location-specific variables that influence ice-related crash frequency and severity.  
[PERSON] et al. [58] employed Tobit regression to treat crash rates as continuous outcomes on Indiana interstates, identifying pavement condition, geometry, and traffic composition as key predictors.  
[PERSON] and [PERSON] [84] compared two-level random-parameter and fixed-parameter negative-binomial models for work zone safety, showing that fixed-parameter specifications can suffice in certain contexts.  
[PERSON] and [PERSON] [85] built a network-wide crash prediction model for rural roads. Roads with \(\leq\)2 m safety zones see 50% more run-off-road crashes, while sharp curves triple run-off-road risk; roadside barriers halve it.  
[PERSON] et al. [63] introduced a count data model with endogenous covariates for urban intersection crashes, finding that crest approaches, frontage-road locations, and flashing-light control substantially increase crash numbers--stressing the value of addressing hidden as well as overt risk factors.  
[PERSON] and [PERSON] [86] documented chronic under-utilization and capacity penalties in California high-occupancy-vehicle (HOV) lanes. They argue that improving overall freeway efficiency, rather than expanding HOV networks, is the more cost-effective path.  
Infrastructure interventions demonstrate a clear effectiveness hierarchy, with physical design modifications significantly outperforming information-based approaches. Electronic toll collection systems achieve the highest safety benefits (72.6% crash reduction), followed by roadside barriers (50% reduction), while ice warning signs show minimal effectiveness. This pattern suggests that passive infrastructure changes that modify driver behavior through design constraints are more effective than active warning systems requiring driver response. The studies reveal important spatial heterogeneity, with [PERSON] et al.'s random-parameter models and [PERSON] and [PERSON]'s safety zone analysis both emphasizing location-specific factors. Methodologically, the comparison between [PERSON] and [PERSON]'s findings and other studies suggests that while sophisticated random-parameter models often outperform fixed-parameter approaches, the context determines optimal model complexity. The failure of HOV lanes to achieve the intended benefits (Kwon and Varaiya) contrasts sharply with the success of toll plaza modifications, highlighting the importance of design compatibility with actual driver behavior rather than idealized usage patterns.