Hi. I modeled a simple 4-legs intersection with traffic lights. With the help of Python I gradually increase the demand volumes, with a certain random margin. At each iteration initially I set the cycle time equal to 60 seconds, the yellow times equal to 3 seconds and the green times equal to 27 seconds, I use Webster's method to determine the optimal cycle time and the green times and compare performance metrics between the two scenarios (edge based, statistics and queues). I'm very intrigued, because most of the time the performance is much worse after optimizing the times. Assuming that my calculations are correct and that the logic is all correctly implemented (I have reviewed everything dozens of times for days), could anyone give any clue to the reason for this strange behavior? Or is it always expected that performance will be better after applying Webster's method, even at microscopic scenarios, and so can only be a problem in my implementation?
I've already experimented with different models of car following, with different values of reaction time (action-step-length) and with different values of lost times (Webster) and nothing helped. Thank you. Alexandre --- Prof. Alexandre Hering Coelho, Dr.-Ing. Departamento de Engenharia Civil Universidade Federal de Santa Catarina Florianópolis, Brasil _______________________________________________ sumo-user mailing list [email protected] To unsubscribe from this list, visit https://www.eclipse.org/mailman/listinfo/sumo-user
