贝叶斯式MCMC与吉布斯采样,用于在预定信号交叉点的异质交通中估计和流量
Lulusi Lulusi1,2, Sugiarto Sugiarto2,3, Sofyan M Saleh2
1Doctoral Program, School of Engineering, Post Graduate Program, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia.
MethodsX
|July 31, 2025
概括
一个新的贝叶斯马尔科夫链蒙特卡洛 (MCMC) 模型显著改善了预定时信号交叉点的基和流速 (BSFR) 估计. 这种先进的方法提高了交通能力的评估,并减少了与现有指南相比高估的情况.
科学领域:
- 交通工程是交通工程.
- 运输科学 运输科学
- 统计建模 统计建模
背景情况:
- 预定时间的信号交叉路口是交通拥堵的主要原因,特别是在交通异质的新兴经济体.
- 准确的基和流量率 (BSFR) 估计对于有效的交叉点容量评估,设计和运行至关重要.
- 目前的印度尼西亚高速公路容量指南 (IHCG, 2023) 使用过时的线性模型 (IHCM, 1997),不适合复杂的交通条件.
研究的目的:
- 开发和验证使用贝叶斯马尔科夫链蒙特卡洛 (MCMC) 方法改进的BSFR估计模型.
- 为了提高在异质流量下信号交叉路口的容量评估的准确性.
- 为了解决印尼背景下现有的BSFR估计技术的局限性.
主要方法:
- 实施贝叶斯马尔科夫链蒙特卡洛 (MCMC) 模型,使用吉布斯抽样进行BSFR估计.
- 与现有的IHCG方法对比拟的贝叶斯MCMC模型的性能.
- 使用诸如根平均平方误差近似 (RMSEA) 和根平均平方误差 (RMSE) 等指标进行统计验证.
主要成果:
- 贝叶斯的MCMC模型实现了8.638%的显著较低的RMSEA,相比IHCG方法的51.428%更低.
- 与IHCG方法相比,开发的模型将BSFR过高估计减少了约42.79%.
- 该模型表现出强大的统计有效性,平均β值为403.30,低蒙特卡洛标准误差 (MCSE) 为0.0008.8.
结论:
- 贝叶斯的MCMC方法为BSFR估计提供了优越的方法,有效地处理异质的交通复杂性.
- 拟议的模型提高了十字路口容量设计精度,并优化了交通管理策略.
- 贝叶斯方法的概率框架提供了可靠的不确定性量化,并减轻了模型过度匹配.
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