Garland Avenue & Cleveland Street

The intersection of Garland Avenue and Cleveland Street, on the University of Arkansas campus, carries high volumes of vehicle and pedestrian traffic, which raises safety concerns for pedestrians crossing it.

Following recent pedestrian incidents, the University started a safety study with Olsson to understand conditions at the intersection and determine whether pedestrians and motorists have enough sight distance to identify and react to potential conflicts safely.

In partnership with Olsson, RDV Systems used RSA 3D to run an Intersection Sight Distance (ISD) analysis. Working from a LiDAR-based 3D model, RSA 3D evaluated visibility across real-world scenarios involving stopped and moving vehicles, buses and pedestrians.

By analyzing multiple vehicle movements, pedestrian paths and potential conflicts within the same 3D digital environment, RSA 3D gave an objective, data-driven assessment of sight distance throughout the intersection. The analysis showed where visibility was adequate, and pinpointed where roadway geometry, vehicles or surrounding features constrained sight lines.

RSA 3D lets engineers evaluate vehicle and pedestrian movements, speeds and sight conditions consistently, within a single 3D environment. Its repeatable, data-driven sight-line analysis identifies visibility constraints and focuses field reviews and design work on the specific approaches, turning movements and crosswalk interactions where safety concerns may exist.

The analysis evaluated multiple conflict scenarios, including stopped and moving vehicles, vehicles and pedestrians in crosswalks, and other vehicle-pedestrian interactions. Garland Avenue was analyzed at 25 mph and 35 mph, and Cleveland Street at 20 mph. For each one, RSA 3D reports an ISDQuality value: the share of the line-of-sight triangle that is visible, from 0 to 1.0, where 1.0 means 100% visibility.

Raising the speed on Garland Avenue from 25 mph to 35 mph reduced visibility further for the constrained movements. The Cleveland WB to Garland NB right turn dropped from 0.58 to 0.42, and the Cleveland EB to Garland SB right turn from 0.38 to 0.31. These results show how changing operating conditions affect sight distance, and point to the areas that need further review.

RSA 3D extended the analysis beyond vehicle-to-vehicle sight distance to pedestrian visibility, across several scenarios: moving vehicles viewing pedestrians, stopped vehicles viewing pedestrians in crosswalks, and turning vehicles approaching crosswalks. Engineers get one consistent way to evaluate the visibility of both vehicles and vulnerable road users, in the same digital environment.

RSA 3D turns complex roadway data into actionable safety insight, letting engineers evaluate multiple movements, speeds, road users and visibility conditions within a repeatable 3D environment. Using LiDAR, CADD, GIS and other roadway data, it automates safety and geometric analyses so agencies can identify potential deficiencies, focus engineering review, prioritize improvements and make better-informed, data-driven roadway safety decisions.

• Pinpoint constrained sight lines with repeatable 3D visibility rays.
• Compare operating conditions, including speed, turning movement and driver eye position.
• Focus engineering review on geometry, vegetation, roadside objects, crosswalk placement and other influences on visibility.
• Create visual, shareable evidence for designers, safety teams and decision-makers.