/AI_Agent_Traffic_Intersection_Model

This is a traffic simulation utilizing independent AI agents, governed by a set of first order logic rules, to reduce the wait time of traffic while prioritizing public transportation

Primary LanguageJava

This program contains two simulations of the St. Paul and Commonwealth Ave intersection

One Simulation, uses a pre-programmed pattern to change the lights The other uses a series of agents using first order logic rules to change the lights

sample output:

35 Cycle complete

northStraight current light color: red number of cars: 0 changed this turn: true TrainLane queue size: 0 Pedestrian Queue Size: 0 southStraight current light color: red number of cars: 0 changed this turn: true TrainLane queue size: 0 Pedestrian Queue Size: 7 westStraight current light color: green number of cars: 0 changed this turn: true TrainLane queue size: 0 Pedestrian Queue Size: 3 eastStraight current light color: green number of cars: 3 changed this turn: true TrainLane queue size: 0 Pedestrian Queue Size: 0 northLeft current light color: red number of cars: 3 changed this turn: true TrainLane queue size: 0 Pedestrian Queue Size: 0 northLeft current light color: red number of cars: 3 changed this turn: false TrainLane queue size: 0 Pedestrian Queue Size: 0

Average wait time for northStraight: is 5.0 Average wait time for southStraight: is 4.0 Average wait time for westStraight: is 3.0 Average wait time for eastStraight: is 2.0 Average wait time for northLeft: is 2.0 Average wait time for northLeft: is 2.0

Total Avereage for first order logic simulation is: 3.0 Total Avereage for first order logic simulation was better than the prescheduled is: 3.0vs4.83