aselya / ai_agent_traffic_intersection_model Goto Github PK

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