Intersection Level of Service for the Bicycle Through Movement

Abstract

The Florida Department of Transportation (DOT) has initiated multi-modal level-of-service (LOS) methodologies, including that for the bicycle travel mode. It has already adopted a bicycle LOS methodology for the roadway segment portion of the transportation network, the Bicycle Level of Service Model. Florida DOT’s ultimate goal is to develop corridor- and facilities-level LOS methodologies. Toward that goal, Florida DOT sponsored research to develop the first part of an intersection bicycle LOS methodology, the Intersection LOS for the bicycle through movement. This Intersection LOS for the bicycle through movement would provide a measure of the level of safety and comfort experienced by bicyclists riding through an intersection. The Intersection LOS model for the bicycle through movement is based on Pearson correlation analyses and stepwise regression modeling of approximately 1,000 combined real-time perceptions from bicyclists traveling a course through a typical U.S. metropolitan area’s signalized intersections. The study’s participants represented a cross section of age, gender, and geographic origin of the population of cyclists. Although further hypothesis testing is being conducted, the resulting general model for the Intersection LOS for the bicycle through movement is highly reliable, has a high correlation coefficient ( R2 = 0.83) with the average observations, and is transferable to the vast majority of U.S. metropolitan areas. The study reveals that roadway traffic volume, total width of the outside through lane, and the intersection (cross street) crossing distance are primary factors in the Intersection LOS for the bicycle through movement.

A Statistical Approach to the Estimation of Bicycle Level of Service Models for the Cicloruta in Bogota, Colombia

“…This is as contained in the highway capacity manual, a modification of Landis [27] and Landis et al [36]. Our initial rating values and other measurement data are now converted into parameter estimates and coefficients and are therefore comparable to the table values (Table 2).…”

“…Few studies that estimated intersection BLOS exist [30], [36], [37] and even fewer have considered a joint estimation of intersection and segment. Carter et al [30] developed an intersection safety index for North Carolina using the generalised linear models, while Landis et al [36] developed a Pearson Correlation and Factor Analysis-based methodology in estimating intersection LOS for the Florida Department of Transportation (FDoT). Some of these studies provided background for choice of intersection variables which were revised to reflect local transport conditions in Bogota.…”

Solar city, bike city, growth city: governance and energy in Davis, California

“…The City's bicycle coordinator, Tim Bustos, said, "We've really done as much as we can really do for bicycle facilities…we're really approaching build-out" (Bustos 2005). However, increased automobile traffic has rendered obsolete some arterial bike lane designs that were once innovative and effective (Landis et al 2003). In 2005, the City organized a Bicycle Advisory Commission.…”

Capacity of Bicycle Platoon Flow at Two-Phase Signalized Intersection: a Case Analysis of Xi’an City

“…(11) and Eq. (12) and Table 2 also shows the calculation results. On the other hand, we identify the number of bicycles within the maximum flow rate and the correspondent signal cycle.…”

“…Of course, such efforts often cause significant reduction in LOS and capacity performance of intersection-related locations [8,9]. A study by Allen et al [10] highlights the quantitative impacts of total occupancy due to bicycles and pedestrians in reducing saturation flow and ultimately lane group capacity through the videotaping of intersections in Davis and Gainesville, and they also argue that the impact of significant bicycle traffic on decreasing the capacity of signalized intersection may be overestimated according to the prevailing "Highway Capacity Manual" (HCM) approaches that would quantify a composite level of service of intersections under mixed flow environment [11][12][13]. Chen et al (2007) summarize the impact of trapped bicycles on the through vehicular traffic from four types: a. blockage by bicycles at the beginning of the green phase; b. influence of waiting bicyclists; c. influence of traversing bicyclists; and d. influence of trapped bicyclists in the intersection [14].…”