基于概率密度演化理论的动态行程时间可靠性计算模型研究 本期目录 >>
Title: A Dynamic travel time reliability calculation model based on probability density evolution thoery
作者 林徐勋;袁鹏程;霍良安
Author(s): Lin Xu-xun; Yuan Peng-cheng; Huo Liang-an
摘要: 目前大多数行程时间可靠性计算模型仅考虑行程时间静态概率分布,无法刻画其动态随机演化过程。结合交通流动力学模型,本文利用概率密度演化理论建立随机行程时间概率密度演化模型,动态反映道路行程时间可靠性的实时波动;结合数论选点和偏微分方程TVD格式数值解设计了模型的求解算法;对上海某高架路段进行实证分析,并与传统的蒙特卡洛方法进行算法对比。结果表明,模型能够较好地刻画行程时间概率密度在不同时段的随机演化规律,且计算时耗大大低于蒙特卡洛仿真。研究能够为交通管理部门道路行程时间预测提供理论依据和工程实践参考。
Abstract: Due to the uncertainty of transportation system, travel time reliability(referred to as TTR) is being paid more and more attention by both travelers and traffic management department. However the existing TTR calculation models only consider the static situation of traffic flow and less involve the short-time dynamic evolution of TTR during peak hour, thus unable to conduct deep research into the travel time probability density short-time dynamic change process and the TTR dynamic evolution law. The main reason is that the current travel time probability density and TTR calculation methods are largely based on pure data mining instead of combination with essential characters of traffic flow and have tremendous dependency on collected data, thus unable to explain the nature of probability relationship between sample data, causing huge computation time consumption, oversights and instability in final calculation results. The probability density evolution (referred to as PDE) method which is developing gradually in recent years is an effective method for study in nonlinear stochastic systems and provides better ideas for dynamic reliability evolution of stochastic system. Combining both data mining technology and system operation mechanism, PDE can reveal the inner relationship among sample data points, reflect the true essence of stochastic system, reduce demand of sample data size while maintaining the result accuracy and finally reduce the difficulty and computation amount. This research uses the traffic flow dynamics theory to simulate the vehicle moving process and establishes the random travel time probability density evolution model to dynamically depict the evolution trajectory of road TTR. The algorithm is designed using number theoretical selection method and TVD-form partial differential equation numerical solution. An empirical analysis is carried out on part of an highway in Shanghai, the traffic flow data collected is classified using cluster method to fit the probability density distribution of road inflow and outflow rate during different time sections, the dynamic evolution trajectory of travel time probability density during night and evening peak hour to show the different characters of dynamic evolution trend under situations of congested and smooth conditions. Comparations are made with traditional Monte-Carlo simulation in terms of fitting accuracy and computational efficiency. The final calculation results show:(1)In situations of smooth road condition the dynamic stochastic road travel time varies steadily, all probability density curves show tiny differences and the TTR evolution character can be depicted using one single probability distribution function, the TTR curve remains in constant state of high reliability;(2)In situations of congested road condition all probability density curves show significant differences and the TTR evolution character cannot be depicted using one single probability distribution function, the TTR shows a steep shape of decreasing trend and finally down to zero;(3)the computation consumption by PDE is much less than by Monte-Carlo simulation. To sum up, for the first time this research applies PDE method to the field of TTR dynamic evolution, taking stochastic road inflow and outflow rate into consideration, the model established by this research not only can reflect the dynamic TTR evolution laws under different traffic states, but also significantly improve the calculation efficiency, thus providing theoretical basis and practical reference for TTR real-time prediction. In addition, several directions for further research can be identified. Firstly, consider the TTR evolution patterns effected by the intersection signal timing; secondly, Secondly, extend the TTR evolution model to the entire transportation network and analyze the TTR evolution pattern on the whole network level. Thirdly, we research on network congestion-propagation and diffusion pattern to provide optimization strategy for emergency evacuation and TTR attenuation control.
关键词: 概率密度演化;行程时间可靠性;交通流动力学;演化规律
Keywords: Probability density evolution; Travel time reliability; Traffic flow dynamics; Evolution law
基金项目: 71303157;13ZR1458200;BSQD201407
发表期数: 2017年 第3期
中图分类号: 文献标识码: 文章编号:
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