Headway Data Extraction and Highway Capacity Manual Capacity Function Calibration for Roundabouts With Roadside Lidar Data

Abstract

There have been many research efforts analyzing roundabouts to extract the gap acceptance parameters of critical and follow-up headways at the entry lanes, but most have not had automatic methods of outputting the desired headways. This paper proposes an automatic method for extracting headway information at roundabouts with roadside lidar data and demonstrating the use of the headway output for Highway Capacity Manual (HCM) roundabout capacity function calibration. The advantages of using roadside lidar sensors over video cameras are that it is less computationally demanding, has a greater detection range, and is effective in all lighting conditions. Roadside lidar provides high accuracy cloud points of all road users that can be converted into geolocated trajectories using software developed by the University of Nevada, Reno (UNR). Headway is then extracted using an ArcGIS plugin of Python Scripts developed by the authors. Roadside lidar data were collected at one roundabout in Reno, Nevada, United States during the daytime hours, and headway data were extracted from the trajectory output. With rejected, accepted, and follow-up headways extracted, Raff’s Method was employed to extract the critical headway and the average of the follow-up headways was calculated. These values were used to determine the entry capacity at a roundabout. The results for the capacity were validated with industry standard equations, providing evidence for an automatic method to extract crucial operational information. The methodology proposed in this paper opens the door for several more extended studies with roundabouts, vehicle-pedestrian interactions, and on-ramp merging events.