Urban traffic light scheduling for pedestrian–vehicle mixed-flow networks using discrete sine–cosine algorithm and its variants

This study addresses the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks. A macroscopic model, which strikes an appropriate balance between pedestrians’ needs and vehicle drivers’ needs, is employed to describe in a framework. The objective of this minimize total network-wise delay time vehicles pedestrians within given finite-time window, crucial avoid congestion urban road To achieve objective, present first uses well-known optimization solver called GUROBI obtain optimal solution by converting into mixed-integer linear programming. obtained results indicate computational inefficiency large network sizes. overcome inefficiency, three novel metaheuristic methods based on sine–cosine algorithm are proposed. These denoted discrete algorithm, with local search operator, operator memory utilization inspired harmony search. Each these developed hierarchically taking advantages previously method(s) terms better process provide more accurate solutions convergence rate. validate all proposed metaheuristics, extensive experiments carried out using real infrastructure Singapore. Moreover, various performance measures such as statistical results, relative percentage deviation, time, analysis, behavior analysis have been evaluate algorithms. comparison SCA variants done other metaheuristics namely, search, firefly bat artificial bee colony, genetic salp swarm harris hawks optimization. Overall concludes that very efficient solve networks different sizes prediction horizons.