Table 2 Average queueing time and maximum queueing time in burst rate simulation Average queueing time (s) Maximum queueing time (s) FR 48354 97063.5 PR 0.311251 4.36831 AR 0.300435 3.67761.
The developed controlling method was compared with four other methods and showed a good performance in terms of reducing the average and maximum queue lengths, optimizing the given green time amount as needed, and increased the intersection's throughput (increased the given green time utilization).
The optimization objectives include the vehicle stops, the average waiting time, and the maximum queue length of the next intersection.
In addition, a sensitivity analysis with seven different traffic flow levels is conducted to measure the performances (average queue length, maximum queue length, average delay, average stop time, and number of stops per vehicle) of the proposed layout.
The MOE parameters considered in the sensitivity study were average delay, maximum queue length, average queue length, average stop time, and number of stops per vehicle.
Let us define X t as the number of packets in the queue at time t, where M is the maximum queue length (X t ≤M).
We changed the traffic volume entering the network every minute from 30 to 270 and estimated the average waiting time, the number of stops, and maximum queue length of these four methods.
This paper then presents a load balancing algorithm to improve the run time performance in distributed model checking, reduce maximum queue size, and reduce the number of states expanded before error discovery.
The maximum queue length is observed in case of system with gamma service time distribution as compared to system following exponential and Erlangian-2 service time distributions.
Using these indices, the queueing process of vehicles at the intersection with waiting areas can be explicitly formulated, in particular, the dynamic behavior of queues along the time during a cycle can be captured, and the formation of maximum queue length during a cycle can also be measured.
