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|Title:||Urban traffic light scheduling for pedestrian–vehicle mixed-flow networks using discrete sine–cosine algorithm and its variants||Authors:||Gupta, Shubham
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2022||Source:||Gupta, S., Zhang, Y. & Su, R. (2022). Urban traffic light scheduling for pedestrian–vehicle mixed-flow networks using discrete sine–cosine algorithm and its variants. Applied Soft Computing, 120, 108656-. https://dx.doi.org/10.1016/j.asoc.2022.108656||Project:||A19D6a0053||Journal:||Applied Soft Computing||Abstract:||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 the traffic light scheduling problem in a scheduling framework. The objective of this problem is to minimize the total network-wise delay time of vehicles and pedestrians within a given finite-time window, which is crucial to avoid traffic congestion in urban road networks. To achieve this objective, the present study first uses a well-known optimization solver called GUROBI to obtain the optimal solution by converting the problem into mixed-integer linear programming. The obtained results indicate the computational inefficiency of the solver for large network sizes. To overcome this computational inefficiency, three novel metaheuristic methods based on the sine–cosine algorithm are proposed. These methods are denoted by discrete sine–cosine algorithm, discrete sine–cosine algorithm with local search operator, and discrete sine–cosine algorithm with local search operator and memory utilization inspired by harmony search. Each of these methods is developed hierarchically by taking the advantages of previously developed method(s) in terms of a better search process to provide more accurate solutions and a better convergence rate. To validate all these proposed metaheuristics, extensive computational experiments are carried out using the real traffic infrastructure of Singapore. Moreover, various performance measures such as statistical optimization results, relative percentage deviation, computational time, statistical analysis, and convergence behavior analysis have been employed to evaluate the performance of algorithms. The comparison of the proposed SCA variants is done with GUROBI solver and other metaheuristics namely, harmony search, firefly algorithm, bat algorithm, artificial bee colony, genetic algorithm, salp swarm algorithm, and harris hawks optimization. Overall comparison analysis concludes that the proposed methods are very efficient to solve the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks with different network sizes and prediction time horizons.||URI:||https://hdl.handle.net/10356/162256||ISSN:||1568-4946||DOI:||10.1016/j.asoc.2022.108656||Rights:||© 2022 Elsevier B.V. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
Updated on Nov 25, 2022
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