ALBERT

Description: Over recent decades, roundabouts have become increasingly used when building new at-grade intersections or up-grade junctions all over the world. Consequently, control of traffic flows at at-grade intersections and up-grade junctions using roundabouts creates unique design problems. Nowadays, ‘alternative’ types of roundabouts have started to become very popular, especially because of their advantages compared with ‘standard’ roundabouts and standard types of up-grade junctions. Some of these alternative types of roundabouts are two-level roundabouts, which are still currently in the development phase. It is for this reason that they can be called ‘theoretical roundabouts’. Two-level roundabouts are particularly useful in urban and suburban areas with space limitations due to their relatively small footprint. This paper illustrates three new alternative types of two-level roundabouts—‘target’, ‘four flyover’, and ‘roundabout with left and right bypasses’—as well as their functions, capacities, and traffic safety characteristics.

Description: Over the past decades many models for roundabout capacity have been proposed. Attention to this research sector has never stopped and still today new formulations are always being studied, especially in view of their direct application in design practice. This paper reports the first noteworthy results of a research concerning the Modified Chumanov (MC) model, which can be used to estimate the capacity of single-lane roundabout entrances. After a detailed examination of the original model recommended by the Russian guidelines for small urban roundabouts, the paper proposes some extensions which allow using the revised model even for larger intersections. The MC model also includes some parameterizations that allow its application with different road pavement surface conditions (i.e., dry and wet conditions). The MC entry capacity model, as function of circulating flow and other parameters dependent on geometry and environmental conditions, was compared with 15 widespread models considering a typical medium-sized single-lane roundabout. A validation test was carried out considering four capacity–circulating flow datasets from the literature. The proposed MC model showed good flexibility in adapting to data. This flexibility appears better than the most recent models by Highway Capacity Manual, in the absence of local calibration of the psychotechnical parameters.

Description: The main purpose of the research is to evaluate the crest vertical curves radii Rv, not considering a conventional value of the opposing vehicle height h2, but the average vehicle heights h2(m) and the value of the 15th percentile of the height distribution h2(15) of the passenger car population. The study only considered car models with more than 20,000 registered vehicles in Italy. One hundred and fifteen car models belonging to different brands were taken into consideration, for a total of over 9 million vehicles. For the statistical sample analyzed, the following vehicle heights were estimated: h2(m) = 1.48 m and h2(15) = 1.39 m. The deviations between the crest radii calculated with the Italian standard (h2 = 1.10 m), and those obtained for h2(m) = 1.48 m and h2(15) = 1.39 m are up to 12%. The differences ΔHv between the values of the visible vehicle body height Hv = Hv(t) calculated using, respectively, h2(15) = 1.39 m and h2(m) = 1.48 m are modest. The value h2(m) = 1.48 m could be adopted in order to reduce the highways construction costs. In fact, the research shows that the value h2 = 1.10 m is too conservative and leads to oversizing of the crest vertical curves. Therefore, it would be necessary to make an appropriate choice of h2 value in order to take into account the current heights of passenger cars.

Description: Macroscopic traffic flow variables estimation is of fundamental interest in the planning, designing and controlling of highway facilities. This article presents a novel automatic traffic data acquirement method, called MOM-DL, based on the moving observer method (MOM), deep learning and YOLOv3 algorithm. The proposed method is able to automatically detect vehicles in a traffic stream and estimate the traffic variables flow q, space mean speed vs. and vehicle density k for highways in stationary and homogeneous traffic conditions. The first application of the MOM-DL technique concerns a segment of an Italian highway. In the experiments, a survey vehicle equipped with a camera has been used. Using deep learning and YOLOv3 the vehicles detection and the counting processes have been carried out for the analyzed highway segment. The traffic flow variables have been calculated by the Wardrop relationships. The first results demonstrate that the MOM and MOM-DL methods are in good agreement with each other despite some errors arising with MOM-DL during the vehicle detection step due to a variety of reasons. However, the values of macroscopic traffic variables estimated by means of the Drakes’ traffic flow model together with the proposed method (MOM-DL) are very close to those obtained by the traditional one (MOM), being the maximum percentage variation less than 3%.