Lulusi Lulusi, Sugiarto Sugiarto, Saleh Sofyan M, Isya Muhammad, Rusdi Muhammad, Rahma Roudhia
Doctoral Program, School of Engineering, Post Graduate Program, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia.
Department of Civil Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia.
MethodsX. 2025 Jul 16;15:103507. doi: 10.1016/j.mex.2025.103507. eCollection 2025 Dec.
Pretimed signalized intersections significantly contribute to traffic congestion, especially under the heterogeneous traffic conditions commonly observed in emerging economies such as Indonesia. Accurate estimation of the base saturation flow rate (BSFR) is essential for reliable capacity assessment, which influences effective intersection design and operation. However, the current BSFR estimation methods outlined in the Indonesian Highway Capacity Guidelines (IHCG, 2023) rely on outdated linear models derived from the Indonesian Highway Capacity Manual (IHCM, 1997), which are inadequate for addressing contemporary heterogeneous traffic complexities. This study introduces a Bayesian Markov Chain Monte Carlo (MCMC) model employing Gibbs sampling to improve BSFR estimation accuracy. The Bayesian MCMC model achieved a Root Mean Square Error Approximation (RMSEA) of 8.638 % compared to the existing IHCG method, which produced an RMSEA of up to 51.428 %, enabling a more precise intersection capacity design. Additionally, the developed model reduced the BSFR overestimation associated with the IHCG method by approximately 42.79 %, highlighting the potential of Bayesian MCMC methods to effectively address heterogeneous traffic challenges, enhance traffic management strategies, and optimize intersection operations. The Bayesian approach provides a probabilistic framework for quantifying uncertainty, allows for the incorporation of prior knowledge to enhance parameter estimation flexibility, and effectively mitigates model overfitting. The developed model demonstrates robust statistical validity, characterized by a mean beta parameter value of 403.30, standard deviation of 8.66, and Monte Carlo Standard Error (MCSE) of 0.0008, confirming high reliability and predictive precision. The proposed BSFR model exhibited superior performance in fitting empirical data, as evidenced by an RMSE of 240.403 PCU/g/h/We and RMSEA of 8.638 %, indicating an excellent model fit within acceptable thresholds (<10 %).
定时信号控制交叉口对交通拥堵有显著影响，尤其是在印度尼西亚等新兴经济体常见的异质交通条件下。准确估计基本饱和流率（BSFR）对于可靠的通行能力评估至关重要，这会影响交叉口的有效设计和运行。然而，《印度尼西亚公路通行能力指南》（IHCG，2023）中概述的当前BSFR估计方法依赖于从《印度尼西亚公路通行能力手册》（IHCM，1997）衍生而来的过时线性模型，这些模型不足以应对当代异质交通的复杂性。本研究引入了一种采用吉布斯抽样的贝叶斯马尔可夫链蒙特卡罗（MCMC）模型，以提高BSFR估计的准确性。与现有的IHCG方法相比，贝叶斯MCMC模型的均方根误差近似值（RMSEA）为8.638%，而现有IHCG方法的RMSEA高达51.428%，从而能够进行更精确的交叉口通行能力设计。此外，所开发的模型将与IHCG方法相关的BSFR高估降低了约42.79%，凸显了贝叶斯MCMC方法有效应对异质交通挑战、加强交通管理策略和优化交叉口运行的潜力。贝叶斯方法提供了一个用于量化不确定性的概率框架，允许纳入先验知识以增强参数估计的灵活性，并有效减轻模型过拟合。所开发的模型具有强大的统计有效性，其平均β参数值为403.30，标准差为8.66，蒙特卡罗标准误差（MCSE）为0.0008，证实了高可靠性和预测精度。所提出的BSFR模型在拟合经验数据方面表现出卓越性能，RMSE为240.403辆客车单位/克/小时/车道组，RMSEA为8.638%，表明在可接受阈值（<10%）内模型拟合良好。
