The Role of Parallel Processing in Dynamic Assignment of Urban Public Transport Considering Capacity Constraint

Considering that the issue of choosing the passenger's route and the parameters involved in it has been the focus and study of transportation planners and policymakers for decades, in this article it has been tried to increase the computational efficiency and reduce the data processing time by examining and improving the mathematical model of passenger route selection in previous studies. This issue has been addressed through multi-core data processing (parallel processing) based on the modification of mathematical models presented in previous studies in the form of a public transport dynamic assignment model with the shortest path algorithm based on the schedule and the travel elimination sub-algorithm. The results were compared with the outputs of a non-dynamic model based on the shortest link-based algorithm to measure the effect of considering the capacity constraint and dynamics of the algorithm on the calculation time and the accuracy of the output. Even though the number of calculations went up by 13.7% compared to the basic model, the time it took to solve the problem went down by 20% because of parallel processing.