Automated Intersection Management with MiniZinc
This addresses traffic flow inefficiencies for urban areas, but appears incremental as it builds on existing constraint satisfaction methods.
The paper tackles traffic congestion at intersections by proposing an automated management system that uses MiniZinc to model it as a constraint satisfaction problem, resulting in reduced mean waiting time and standard deviation of waiting time for vehicles while avoiding deadlocks.
Ill-managed intersections are the primary reasons behind the increasing traffic problem in urban areas, leading to nonoptimal traffic-flow and unnecessary deadlocks. In this paper, we propose an automated intersection management system that extracts data from a well-defined grid of sensors and optimizes traffic flow by controlling traffic signals. The data extraction mechanism is independent of the optimization algorithm and this paper primarily emphasizes the later one. We have used MiniZinc modeling language to define our system as a constraint satisfaction problem which can be solved using any off-the-shelf solver. The proposed system performs much better than the systems currently in use. Our system reduces the mean waiting time and standard deviation of the waiting time of vehicles and avoids deadlocks.