Passive acoustic monitoring (PAM) is a valuable tool in wildlife research, offering non-invasive and cost-effective means to collect acoustic data. However, the processing of PAM recordings can be time-consuming, prompting the use of acoustic indices to expedite analysis. Acoustic indices are numerical values that characterize biological information in sound recordings based on an environment’s acoustic characteristics. While acoustic indices have been correlated with species richness across ecological contexts, their reliability diminishes in areas with heightened vehicular noise. However, it is unclear if index biases caused by vehicular noise are consistent across all traffic levels, and which acoustic indices are most biologically informative in these human-developed contexts. I assessed the direct impact of vehicular noise on nine acoustic indices through controlled manipulation of vehicular noise within 598 computer-generated bird assemblage soundscapes. Using the bird assemblage soundscapes, I also investigated the effects of three high-pass filter treatments (482 Hz, 1 KHz, and 2 KHz) on these acoustic indices under different levels of traffic noise interference. These filtering effects were also assessed within empirical PAM recordings taken from 147 sites in southern Illinois from May into mid-July of 2022 and 220 sites across the state during late-April to mid-July of 2023. Results indicate that proximity to roads and vehicular traffic significantly affect index values, albeit to varying degrees. Four indices – Bioacoustic Index, Acoustic Complexity Index, Acoustic Diversity Index, and Acoustic Evenness Index – exhibited greater resilience to vehicular noise and may be better suited for urban environments. Notably, the Acoustic Diversity Index, Acoustic Evenness Index, and the Number of Frequency Peaks also displayed consistent species richness estimations regardless of vehicular noise level. While filtering had variable interactive effects with vehicular noise, no consistent benefits of filtering were observed across all indices. Nevertheless, the Acoustic Complexity Index, Acoustic Richness Index, and CityBioNet displayed minimal biases when high-pass filters were applied, and CityBioNet demonstrated particularly high correlations with species richness. These findings underscore the importance of understanding index behavior under anthropogenic noise and different filtering methodologies. My findings serve to inform acoustic index implementation within acoustic monitoring efforts, thus expanding access and reliability of these methodologies within human-developed environments.