不利天气下离散化动态交通路网容量研究

为了制定合理的不利天气下交通管理方案,采用离散化动态交通路网容量描述道路网容量变化情况.定义了不利天气下离散化动态路网容量,提出了与时空消耗概念相类似的基于通行能力的离散化动态路网容量计算模型,并对模型中的通行能力和平均出行距离两个变量进行了说明:采用径向基函数神经网络模型处理不利天气下通行能力与众多非线性影响因素;用概率统计理论推导出行距离的概率密度函数,概率密度函数的均值即为交通平均出行距离.通过对哈尔滨市暴雨天气下道路实际数据的分析,验证了模型的可行性和有效性.

discrete dynamic road network capacity under adverse weather

For the sake of establishing a reasonable traffic management scheme under adverse weather, adopting discrete dynamic traffic network capacity to describe the changes of the road network capacity. The concept of discrete dynamic road network capacity under adverse weather was defined and the discrete dynamic mathematical model was put forward based on traffic capacity, which has similarity with spatio-temporal dissipation concept. Two variables, traffic capacity and average trip distance, were explained. Radial Basis Function Neural Network was applied to dispose the relationship between traffic capacity and many non-linear influencing factors under adverse weather. Based on probability and statistics theory, the probability distribution function of trip distance was deduced, and the expectation of this function is the average trip distance. Through the calculative example analysis of the actual road data under storm weather in Harbin, which further validated the feasibility and validity of the model.