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  

A Dynamic travel time reliability calculation model based on probability density evolution theory

作　　者：林徐勋[1] 袁鹏程[2] 霍良安 LIN Xu-xun YUAN Peng-cheng HUO Liang-an(School of Economics and Management, Changzhou University, Changzhou 213164, China Business School, University of Shanghai for Science ＆ Technology, Shanghai 200093, China)

机构地区：[1]常州大学商学院,江苏常州213164 [2]上海理工大学管理学院,上海200093

出　　处：《管理工程学报》2017年第3期142-148,共7页Journal of Industrial Engineering and Engineering Management

基　　金：国家自然科学基金资助项目(71303157);上海市自然科学基金资助项目(13ZR1458200);上海理工大学博士启动资金资助项目(BSQD201407)

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。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 involve less 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 essential characters of traffic flow and the collected data, thus unable to explain the nature of probability relationship between sample data. These restrictions can cause huge computation time consumption, oversights and instability in final calculation results. The probability density evolution（referred to as PDE） 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, and reduce the demand of sample data size while maintaining the result accuracy and finally reducing 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 theoretical selection method and TVD-form partial differential equation numerical solution. An empirical analysis is carried out on parts of a highway in Shanghai. The traffic flow data collected is classified using the cluster method to fit the probability density distribution of road inflow and outflow rate during different time sections, and t

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 

分 类 号：U491[交通运输工程—交通运输规划与管理]