## 6 Conclusions  
This study integrates city-wide near-misses to estimate urban traffic safety and analyzes its spatial patterns. Grid-based aggregation method, the Empirical Bayes (EB) approach, and spatial analysis tools including global Moran's I and local Moran's I were applied to conduct this study. The findings suggest that near-misses have the highest correlation with crash frequency among all the variables. Other variables such as the number of intersections, number of bus stops, road length, residential land use rate, and open-space land use rate significantly influence crash frequency in New York City. The near-miss-to-crash ratio, estimated to be 1957:1 for the study urban area, can serve as a potential benchmark for other urban environments. The analysis of network and facility-related variables revealed that a higher number of intersections and bus stops, and longer road length, all contribute to an increased crash frequency. Residential and open-space land use rates, on the other hand, were negatively correlated with crash frequency, likely due to lower traffic volumes, fewer complex intersections, and more traffic calming measures in these areas. Spatial analysis highlighted areas with high and low crash frequencies, revealing potential risk hotspots, such as linkage roads of bridges and tunnels, and avenues with high pedestrian activity. It was interesting to observe negative local spatial correlations in crash frequencies, pointing to significant variations in safety risks within short distances. The observation and prediction difference map further identified grids with unexpectedly high or low crash frequencies, offering valuable insights for targeted interventions. Based on the findings, several detailed policy recommendations can help increase traffic safety in urban environments. Enhancing near-miss data collection and analysis by implementing city-wide near-miss reporting systems using mobile applications and connected vehicle technologies may provide comprehensive data for predictive models, identifying high-risk locations before crashes occur. Improving intersection safety remains one of the top priority actions in urban areas. Optimizing bus stop locations by conducting safety audits and relocating high-risk stops is. Encouraging residential and open space land use in urban planning may naturally improve safety. Targeted safety interventions in identified high-risk areas, such as linkage roads of bridges and tunnels, and high pedestrian activity avenues, can address specific safety concerns. This research has potential implications for urban transportation safety policy and practice. It highlights the value of incorporating near-miss data collection and analysis into safety estimation. Understanding crash prediction and spatial distribution patterns can help city planners and policy makers devise and implement location-specific strategies for improving road safety. Although the study offers valuable insights, certain limitations and future studies are presented. Although we can filter Vulnerable Road User (VRU)-related data from both crash and near-miss data, there are too few near-misses for VRUs to conduct statistical analysis. It would be interesting to check the estimated traffic safety for the two most common VRUs, pedestrians and cyclists, in the future. The variables used in this study, although comprehensive, do not account for all potential factors that may influence crash frequency, for example, curve and interchange. Further, the NB model, though it works well for most grids, may not accurately represent grids with complex road networks. Future research could refine and expand upon this work by incorporating more variables, considering other modeling techniques, and extending the research to other urban environments. Moreover, studying driver behavior in near-miss events is crucial in the future, as it may significantly impact the relationship between near-misses and actual crashes. In addition, some conclusions could be further supported by additional data and analysis.