Assessing the Impact of Urban Trip Attraction Centers on Urban Street Safety Using Drone-Derived Traffic Data

• Trip attraction centers influence driver behavior and micro-scale safety indicators. • On-street parking occupancy shows non-linear, threshold effects on surrogate safety metrics. • Combined geometric design, access, and parking strategies reduce traffic conflicts. • Data-driven framework enables predictive safety audits for urban planning decisions. • GAM and regression modeled nonlinear traffic performance impacts. Urban Trip Attraction Centers (UTACs) are key determinants of urban street safety, generating concentrated vehicle maneuvers and curbside interactions that increase collision risk during peak traffic periods and thereby strongly affect road user safety. This study examines the effects of UTACs on traffic safety indicators at the microscopic scale using high resolution traffic data collected by unmanned aerial vehicles (UAVs) from arterial streets in Dezful, Iran, during morning and evening peak periods. To identify complex and nonlinear relationships between traffic variables and safety indicators, and based on extensive evidence regarding the importance of flexible and nonlinear models, the Generalized Additive Model (GAM) was employed. The results indicate that with increasing access demand to UTACs and higher on-street parking occupancy at these centers, Time to Collision (TTC) decreases nonlinearly and collision risk increases; the rate of TTC reduction is particularly accelerated at moderate to high curbside parking occupancy levels (20% to 60%). Analyses also show that for every 0.1 increase in on-street parking occupancy at UTACs, mainline vehicle speeds approximately 10 meters downstream of the center decrease by 4.4 km/h, and vehicle headways at the downstream exit also decrease nonlinearly. This study demonstrates that UTACs can substantially affect road user safety during peak traffic hours through increased demand and intensified roadside activities along urban arterials. Moreover, this research provides a robust framework and methodology for linking land use intensity and operational conditions with driver behavior and traffic safety, offering practical insights for traffic management and urban planning.