Adaptive Signal Control of an Isolated Intersection Using Stop-Line Detection

Abstract

Adaptive traffic signal controllers offer better signal time management especially when the traffic flow pattern is not uniform on all approaches. Traditional adaptive traffic controller use upstream or advance vehicle detection which works well in situations where traffic follows good lane discipline. However, when the spacing between intersections increases or in the case of complex geometry these systems may not be efficient. This is primarily because of the inability of traffic flow models to accurately estimate the traffic demand from the upstream detectors. Using stop-line detector information is best suited in such traffic conditions as they do not require any explicit prediction models. Furthermore, there are many intersections which works using stop-line detectors with preset maximum green timings as vehicle actuated controllers. These controllers can be easily converted into truly adaptive by changing their maximum green timings continuously with respect to changing traffic flow pattern. Hence, this paper proposes an adaptive traffic control model which uses stop-line detector information instead of upstream detector. The model aims at real-time allocation of green time through reinforcement learning; an approach originated from the machine learning community. This approach has the ability to learn relationships between signal control actions and their effect on the queue while pursuing the goal of maximizing throughput which is a distinct improvement over the traditional vehicle actuated system. To demonstrate the performance of the proposed model a typical four-way intersection with four-phase scheme is evaluated for various flow conditions with the proposed model as well as with the traditional vehicle actuated system. The results show improvement over traditional system, especially when the flow is near the capacity.