An integrated intelligent intersection management and control system design

Abstract

While a fair amount of study has been done recently on integrated management and control models for large-scale traffic systems, there is comparatively little information available regarding the design of management and control systems for small-scale intersections. This study aims to create the structural diagram of a management system for intersections and control technologies. It does this by designing three distinct systems: congestion prediction, traffic signal timing, and congestion flow dredging. This study searches the current state of common technology and chooses the intersection integrated management system by means of comparison. Fuzzy TOPSIS algorithm and ADP adaptive dynamic programming are chosen as the calculation techniques of signal lamp timing, while BP neural network and ATT-GCN network are chosen as the key algorithms of congestion prediction after thorough investigation. In the end, conventional traffic management techniques like forbidding left turns are used to direct cars that haven't arrived at the busy crossroads beforehand. This paper aims to select the best model for an intersection management system currently available from the common technologies. The idealized management system and its structure diagram are designed in a straightforward manner, and they will serve as a guide for future road and vehicular intersection design.