Estimation models of distribution functions for travel time value with maximum entropy principle
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摘要: 为提高使用时间价值数据拟合其统计分布的精度, 将最大熵原理分别与低阶(≤6)经典矩和概率加权矩相结合, 建立了时间价值低阶经典矩与概率加权矩统计分布函数模型。仿真结果表明: 在大样本量下, 利用经典矩与概率加权矩对参数估计的精度相当, 在小样本量下(< 30), 采用概率加权矩估计参数的相对误差在10%~35%之间, 而采用经典矩估计参数的相对误差在20%~80%之间。可见利用概率加权矩克服了经典矩模型在小样本量下参数估计的大偏差问题, 且利用其可以准确地预测交通方式分担率与分析交通定价政策对交通行为的影响。Abstract: In order to improve the goodness-of-fit accuracy of statistical distribution for travel time value with its stylebook data, maximum entropy principle was combined with probability-weighted moments (PWM) and statistical ordinary moments (SOM)(≤6) respectively, two new statistical distribution models were proposed, and parameter estimation was carried out by using probability-weighted moments and statistical ordinary moments. Simulation result shows that when the number of stylebooks is less than 30, the average relative errors between exact values and estimating values range from 10% to 35% and from 20% to 80% respectively, but the statistical accuracies are similar on large sample size condition, so the estimating accuracy with probability-weighted moments is higher. Obviously, the proposed methods are useful to promote parameter estimating accuracy, may predict the split ratios of traffic modes effectively, and analyze the influence of transportation pricing policy on travel behaviors.
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