Passenger Car Equivalent (PCE) factors are widely used to convert heterogeneous traffic streams into equivalent homogeneous flow rates for the design and analysis of roads and intersections. In developing countries, mixed traffic conditions differ substantially from those in developed contexts due to variations in vehicle composition, operating characteristics, roadway parameters, and environmental conditions. Consequently, PCE values are highly context-specific and require periodic updates to accurately represent prevailing traffic conditions. However, such updates are often infrequent because conventional PCE estimation relies on extensive field data collection through time-consuming and costly traffic surveys, as well as the availability of experienced experts to conduct and validate the analyses. In Sri Lanka, the currently adopted PCE factors are more than two decades old and no longer reflect existing traffic conditions. Although several recent studies have estimated PCE values for mid-block roadway sections of various facility types (e.g., four-lane roads, two-lane roads, and freeways), no study has comprehensively addressed intersections, which are critical for signal timing and geometric design. This study aims to develop a systematic methodology for estimating intersection-specific PCE factors using drone-based video data. Traffic data were collected at selected intersections using an unmanned aerial vehicle to obtain an accurate bird’s-eye view of vehicle movements. The methodology compares the area occupancy of different vehicle categories under varying traffic compositions with that of a passenger-car-only traffic stream operating at the same average speed. Using the extracted traffic parameters, the basic headway method was applied to establish a framework for calculating PCE factors. PCE values were estimated for ten vehicle categories, and the results reveal significant deviations, particularly for three-wheelers, motorcycles, and commercial vehicles, when compared with values currently in use. A high-level comparison with studies from other developing countries in the South Asian region indicates notable differences in vehicle impacts at signalized intersections in Sri Lanka. Furthermore, the proposed methodology provides a practical, economical, and less labour-intensive approach for estimating PCE factors, enabling more frequent updates without requiring extensive field surveys or specialized expertise. Because it relies on a straightforward headway-based framework and drone-derived traffic data, the methodology can be readily adapted to different roadway facilities, including highways, rural roads, and intersections, making it suitable for application across diverse geographical regions.

This study evaluates driver yielding behavior toward pedestrians at roundabout crosswalks in Minnesota, comparing base-case crossings against three specialized crossing treatments (in-roadway signs, rectangular rapid flashing beacons (RRFBs), and colored crosswalks) and testing which site and treatment characteristics are statistically associated with yielding. A statewide survey of 91 Minnesota city and county engineers identified candidate roundabouts with known pedestrian concerns. Video cameras were installed at 15 roundabouts (approximately 50 h of daytime footage per leg), and eight roundabouts were selected for detailed case study analysis of vehicle–pedestrian interactions, following observational protocols adapted from prior driver yielding research; the resulting leg-level dataset comprises 22 treatment-condition observations across 18 roundabout legs. Each pedestrian crossing event was coded for driver yielding behavior (active yield, passive yield, no yield) and pedestrian crossing behavior (normal, hesitates, retreats, runs, not ready). To complement this descriptive analysis and enable formal statistical inference from a limited number of sites, a beta regression model was fit to leg-level overall yielding rates as a function of specialized crossing treatment, roundabout configuration, total lanes, approach speed, circulatory roadway speed, traffic volume, and pedestrian count. Descriptively, single-lane roundabouts showed higher overall yielding (85.7–100%) than multi-lane roundabouts, and exit-lane yielding was consistently lower than entry-lane yielding across all sites. RRFBs achieved close to 100% compliance when activated by pedestrians, but compliance fell by 15–38 percentage points when pedestrians did not activate the beacon. Colored crosswalk sites showed the lowest observed yielding rates (11.1–24.1%). The beta regression model supported and refined these patterns: RRFB activation was associated with 4.76 times higher odds of yielding (p = 0.0015), each additional lane was associated with 49% lower yielding odds (p = 0.022), and 2 × 1 roundabout configurations showed 76% lower yielding odds than 1 × 1 configurations (p = 0.0085). Colored crosswalks were associated with significantly lower yielding odds in the simplified model (p = 0.004). Approach speed, traffic volume, and pedestrian count were not statistically significant once treatment and configuration were accounted for, a finding that qualifies speed-focused interpretations of the descriptive results. This study pairs a statewide Minnesota case study dataset with a formal inferential layer, a beta regression model appropriate for bounded proportion outcomes, and is, to the authors’ knowledge, the first application of beta regression to driver yielding compliance data at roundabout pedestrian crossings. The finding that colored crosswalk sites showed the lowest yielding among the treatments studied, based on two sites and best treated as a preliminary association rather than a conclusive treatment effect, together with the confirmation that RRFB effectiveness depends on pedestrian activation, offer transportation agencies early, statistically grounded evidence to inform selecting and pairing pedestrian crossing treatments at roundabouts as installations of this proven intersection form continue to expand nationally.