## 1 Introduction  
According to the World Health Organization, approximately 1.35 million people die in road traffic crashes annually [1], making it the eighth leading cause of death worldwide. Furthermore, an additional 20 to 50 million individuals suffer non-fatal injuries [2], often resulting in long-term disabilities. In densely populated urban areas like New York City, traffic safety is worsened by the sheer volume of vehicles, pedestrians, and cyclists that share the streets. With over 259 fatalities and 50,733 injuries reported in 2022 [3], the city faces numerous challenges, including the high cost of accidents, the vulnerability of certain road users, and the impact of crashes on traffic congestion. Addressing these issues is of utmost importance for the well-being of the city's residents, commuters, and visitors. Fortunately, a unified commitment to promoting safety for all is demonstrated at both federal and city levels. The Federal Highway Administration's (FHWA) Strategic Plan for 2022-2026 [4] places traffic safety at the forefront of its goals, emphasizing five key components: Safety Design, Safety System, Safe Public, Safe Workers, and CriticalInfrastructure Cybersecurity. This comprehensive approach highlights the need for innovative infrastructure solutions, data-driven methodologies, public awareness, worker safety, and cybersecurity measures to address the multifaceted challenges of traffic safety. New York City's Vision Zero Plan [5], launched in 2014, is another key initiative that shares the objective of improving traffic safety. The plan aims to eliminate traffic-related fatalities and serious injuries by implementing a combination of enforcement, education, and engineering measures. Despite a nationwide rise in traffic fatalities, New York City defied the trend in 2022 with a 6.6% reduction in overall traffic fatalities, and a 6.3% reduction in pedestrian fatalities, with Vision Zero in effect. These federal and city-level initiatives spotlight the collective responsibility to prioritize traffic safety. Historical crash data play a critical role in traffic safety analysis, as they provide a foundation for understanding the factors contributing to crashes, identifying high-risk areas, and countermeasure assessment. However, despite their value, the rarity of crashes, the issue of underreporting, and low location accuracy limit their usage in practice. Using solely crash data to estimate safety often suffers from the regression-to-the-mean problem [6; 7]. The Empirical Bayes (EB) method [6; 7] is a potential solution because it can combine the observed crash frequency from the real world and the expected crash frequency predicted by a crash prediction model. It is well suited for crash frequency estimation, given that it accounts for site-specific effects and regression-to-the-mean bias. A reliable crash prediction model can be a solid foundation for the EB method. Furthermore, integrating valuable information such as near-misses [8; 9; 10; 11] to build a crash prediction model can be a potential improvement. With the advancement of computer vision technology, devices, either via fixed infrastructures (e.g., roadside cameras) or onboard devices (e.g., in-vehicle cameras, radars), have emerged as valuable tools in detecting near-misses. These devices can capture near-miss incidents, providing a more comprehensive understanding of potential hazards and close interactions among road users that might not result in reported crashes. With the anticipated rise of autonomous and connected vehicles, the collection of near-miss data is expected to become both more feasible and cost-effective. As such, the effective analysis of these easily acquired near-miss data can yield insights that go beyond individual crashes, playing a crucial role in enhancing road safety estimations. This study integrates city-wide near-misses to estimate urban traffic safety and analyzes its spatial patterns.