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Comparison of artificial bee colony and flower pollination algorithms in vehicle delay models at signalized intersections

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Abstract

Delay is a significant research topic since it includes indicators such as travel quality, lost time and fuel consumption. Furthermore, the delay is used for optimization of traffic control systems and determination of the level of service at signalized intersections. Therefore, researchers have focused on accurate estimation of delay. The objective of this study is to simply and accurately estimate the delay and evaluate the performance of the proposed approaches which are artificial bee colony (ABC) and flower pollination algorithms (FPA). In this study, ABC and FPA have been used to develop different delay models which are linear, semi-quadratic, quadratic and power forms. Analysis period (T), the green ratio (g/C; effective green to cycle length) and the degree of saturation (x = v/c; volume to capacity) are used as input parameters while developing the models. The results of present models are compared to estimations obtained from analytical models which are Highway Capacity Manual and Australian (Akçelik) delay models. Semi-quadratic form yielded to best results in terms of coefficient of determination (R2), mean square error and mean absolute error. Additionally, FPA approach showed better performance than ABC approach finding the optimal solution in the lower number of iterations.

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Authors and Affiliations

  1. Department of Civil Engineering, Engineering Faculty, Kirikkale University, 71451, Kirikkale, Turkey

    Ersin Korkmaz & Ali Payıdar Akgüngör

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  1. Ersin Korkmaz
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  2. Ali Payıdar Akgüngör
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APA contributed to the literature search and review, manuscript writing and editing. EK developed the models and analysed it, and also contributed to the materials and methods (algorithms search and review), content planning and manuscript writing.

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Correspondence to Ersin Korkmaz.

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Korkmaz, E., Akgüngör, A.P. Comparison of artificial bee colony and flower pollination algorithms in vehicle delay models at signalized intersections. Neural Comput & Applic 32, 3581–3597 (2020). https://doi.org/10.1007/s00521-018-3670-3

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