The aim of this study was to develop three roundabout entry capacity models (RECMs) by employing evolutionary-based regression techniques such as genetic programming (GP), age-layered population structure genetic programming (ALPSGP) and grammatical evolution genetic programming (GEGP) in mixed traffic conditions. Necessary data were collected from 27 roundabouts located in eight states in India. The influence area for gap acceptance method was used to determine the critical gap. To assess the significance of the models and select the best, the modified rank index was applied. The results showed that the GEGP model performed better than the GP and ALPSGP models. The GEGP model is also applicable in practice because of its simplicity. Sensitivity analysis revealed that the critical gap is the prime variable in the development of RECMs. The findings of this study should be useful for traffic planners and designers in the capacity estimation of roundabouts in mixed traffic conditions in developing countries with traffic characteristics similar to those in India.

中文翻译：

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回旋处进入能力模型：遗传编程方法

本研究的目的是通过采用基于进化的回归技术，如遗传编程 (GP)、年龄分层种群结构遗传编程 (ALPSGP) 和语法进化遗传编程 (GEGP) 来开发三种迂回进入能力模型 (RECM)。混合交通条件。从位于印度八个邦的 27 个环形交叉路口收集了必要的数据。间隙接受方法的影响区域用于确定临界间隙。为了评估模型的显着性并选择最佳模型，应用了修正的等级指数。结果表明，GEGP 模型的性能优于 GP 和 ALPSGP 模型。GEGP 模型由于其简单性也适用于实践。敏感性分析表明，关键差距是 RECM 发展的主要变量。