Optimization of Autonomous Rail Rapid Transit at Arterial-Branch Intersection: A Fuzzy Control with Borrowable Lane Approach

With the development of emerging communication and control technology, more and more Autonomous Rail Rapid Transit (ART) are put into operation. Signal control is critical for ART to ensure the efficiency and safety of the intelligent rail operation at intersections. In order to better adapt to the signal control when the ART passes through the Arterial-Branch intersection, this paper proposed a control method. That was, the flow ratio of branch road to arterial road and the ratio of total flow to saturation flow of the intersection were taken as inputs, the initial signal cycle was calculated by a fuzzy controller, and then the complete signal timing scheme was calculated by the Webster algorithm. As there was no physical isolation from the ordinary motorway for the ART, we set up a special indicator light for the approaches. When the ART was passing through, the intersection signal timing was adjusted; otherwise, other vehicles were allowed to drive in the ART lane. Python and VISSIM were jointly employed in the simulation. The original data was based on a traffic volume survey of an Arterial-Branch intersection in Harbin on weekdays, and the ART was assumed to be in operation. Results show that, compared with the fixed timing and simple fuzzy control, this method improved the performance of the intersection operation in terms of the evaluation indexes of vehicle queue length, average delay and stopping times. The findings of this study could also provide a research basis for the mixed traffic flow of autonomous driving and manual driving at the Arterial-Branch intersections.