Impact of Geometric Factors on the Capacity of Single-Lane Roundabouts. Issue 34 (December 2018)

• Record Type: Journal Article
• Authors: Johnson, Mark T.; Lin, Ting-Li
• Is Part Of: Transportation research record. Volume 2672:Issue 34(2018)
• Page Start: 10
• Page End: 19
• Publication Date: 2018-12
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/0361198118758309
• Languages: English
• ISSNs: 0361-1981

Abstract

Roundabout capacity is primarily estimated by gap-acceptance or by geometric models. The 2010 Highway Capacity Manual (HCM 10) uses a gap-acceptance model developed by Siegloch with empirically derived values of critical gap and headway for single-lane and two-lane entries. A geometric capacity model was developed by Kimber and Hollis that diverged from gap-based models. The capacity of up to four lane entries was empirically derived from six geometric parameters. In 2012 capacity data were collected as part of the FHWA project, Assessment of Roundabout Capacity Models for the Highway Capacity Manual. These data were used to produce HCM6, an update of the HCM 2010. In HCM6, a capacity curve was fitted through all the capacity data for single-lane roundabouts. The large scatter of data about the mean capacity line suggests that the single-lane roundabouts may be separated into different geometric types to improve accuracy. To investigate this hypothesis, the capacity data were separated into two geometrically distinct types of single-lane roundabouts: smaller, compact and larger, curvilinear. The range of data for the disaggregated and aggregated data was compared. Also, a capacity line was derived for each type using the geometric capacity model and compared with the disaggregated data with the HCM6 method. The results demonstrate that differences in geometry, absent in HCM6, explain the wide data range. This was further confirmed by the geometric model that gave a good fit to both sets of data. These results indicate that the accuracy of capacity prediction is improved by including geometric variation.