A case study on multi-lane roundabouts under congestion:Comparing software capacity and delay estimates with field data

Existing studies on modern roundabouts performance are mostly based on data fron： singe lane roundabouts that are not heavily congested. For planners and designers interested in building multilane roundabouts for intersections with potential growth i~ future traffic, there has been a lack of existing studies with field data that provide reference values in terms of capacity and delay measurements. With the intent of providing such reference values, a case study was conducted by using the East DowlinC Road Roundabouts in Anchorage, Alaska, which are currently operating with extensive queues during the evening peak hours. This research used multiple video camcorders t（ capture vehicle turning movements at the roundabouts as well as the progressior~ of vehicle queues at the roundabout entrance approaches. With these video records, the number of vehicles in the queues can be accurately counted in any single minute during the peak hours. This study shows that unbalanced entrance flow patterns （i.e., ~ne entrance has significant higher flow than others） can intensify the queue and delay fo., the overall roundabouts. Then various software packages including RODEL, SIDRA and VISSIM were used to estimate several performance measurements, such as capacity. queue length, and delay, compared with the collected field data. With the comparison, it is found that all the three software packages overestimate multi-lane roundabout ca pacity before calibration. With default parameters, SIDRA and VISSIM tend to underes timate delays and queue lengths for the multi-lane roundabouts under congestion, while RODEL results in higher delay and queue length estimations at most of the entrance approaches.

Situation reasoning for an adjustable autonomy system

Purpose–The purpose of this paper is to provide a more capable and holistic adjustable autonomy system,involving situation reasoning among all involved information sources,to make an adjustable autonomy system which knows what the situation is currently,what needs to be done in the present situation,and how risky the task is in the present situation.This will enhance efficiency for calculating the level of autonomy.Design/methodology/approach–Situation reasoning methodologies are present in many autonomous systems which are called situation awareness.Situation awareness in autonomous systems is divided into three levels,situation perception,situation comprehension and situation projection.Situation awareness in these systems aims to make the tactical plans cognitive,but situation reasoning in adjustable autonomous systems aim to communicate mission assessments to unmanned vehicle or humans.Thus,in solving this problem,it is important to design a new situation reasoning module for the adjustable autonomous system.Findings–The contribution of this paper is presenting the Situation Reasoning Module(SRM)for an adjustable autonomous system,which encapsulates event detection,cognitive situations,cognitive tasks,performance capacity assessment and integrated situation reason.The paper concludes by demonstrating the benefits of the SRM in a real-world scenario,a situation reasoning simulation in unmanned surface vehicles(USV)while performing a navigation mission.Originality/value–The method presented in this paper represents a new SRM to reason the situation for adjustable autonomous system.While the results presented in the paper are based on fuzzy logic and Bayesian network methodology.The results of this paper can be applicable to land,sea and air robotics in an adjustable autonomous system.