Queue length estimation for signal controlling in a connected environment

Abstract

Queue length at signalized intersection is an important measure that helps design and operate road networks. Queue length information can be shared with road users allowing them to take choose alternative path to avoid delay. In addition, it can be used to determine storage lane length and spacing between two adjacent intersections to avoid grid blockage. This paper focuses on queue length estimation at signalized intersection using Discrete Event Simulation (DES). The developed model using DES is built using Python programming language based on hypothetical data. The model embraces stochastic behavior for both Arrival and departure vehicles at the intersection by following Poisson. Process with exponential distribution of the inter-arrival time. The simulation is done under six different v/c ratios starting from 0.3 to 0.8 with one tenth increment. The results then compared with microsimulation software VISSIM results of the same intersection. The results from DES model and VISSIM model are close to each other with percentage error of 11%. This percentage represent two vehicles at max in the six scenarios. In addition, the developed model does not need calibration nor validation unlike VISSIM. Moreover, the computation time of the developed model found to be significantly faster with 0.2 s while the microsimulation software need 105 s to complete the simulation at maximum speed considering that both models’ simulation time is one hour.