Time Coefficient Estimation for Hourly Origin-Destination Demand from Observed Link Flow Based on Semidynamic Traffic Assignment
Time Coefficient Estimation for Hourly Origin-Destination Demand from Observed Link Flow Based on Semidynamic Traffic Assignment
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DOI：
10.1155/2017/6495861
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发表时间：
2017-01-01
影响因子：
2.3
通讯作者：
Murakami, Shintaro
中科院分区：
文献类型：
--
作者：
Fujita, Motohiro;Yamada, Shinji;Murakami, Shintaro
由于不受OD分布每小时变化的影响，运输预测的全天始发目的地（OD）需求估计在准确性和可靠性方面具有优势。本文针对缺乏离散时间丰富交通数据的大型路网城市交通规划，提出了一种估算日OD需求时间系数的方法来估算小时OD需求和预测小时流量。该模型基于广义最小二乘和半动态交通分配（OD修正方法）的双层公式，在给定已证实的一天OD需求的情况下，根据观察到的链路流量估计时间系数。基于每一时段末的剩余需求，将od修正方法表述为具有弹性需求的静态用户均衡分配。我们的模型不需要设置关于OD需求矩阵和离散时间动态交通分配的许多参数。将该模型应用于大尺度路网，由于调查数据的24小时时间系数偏差较小，可以适当修正，有效地提高了估计精度。此外，还研究了部分放宽OD修正法的假设，将估计需求转化为基于出发时间的需求的方法。
Day-long origin-destination (OD) demand estimation for transportation forecasting is advantageous in terms of accuracy and reliability because it is not affected by hourly variations in the OD distribution. In this paper, we propose a method to estimate the time coefficient of day-long OD demand to estimate hourly OD demand and to predict hourly traffic for urban transportation planning of a large-scale road network that lacks discrete-time rich traffic data. The model proposed estimates the time coefficients from observed link flows given a proven day-long OD demand based on a bilevel formulation of the generalized least square and semidynamic traffic assignment (OD-modification approach). The OD-modification approach is formulated as a static user-equilibrium assignment with elastic demand, based on the residual demand at the end of each period. Our model does not require setting many parameters regarding the OD demand matrices and the discrete-time dynamic traffic assignments. Applying the model to large-scale road network demonstrates that it efficiently improves estimation accuracy because the 24-hour time coefficients of survey data are slightly biased and may be modified properly. In addition, the methods that partially relax the assumption of OD modification approach and transform the estimated demand into demand based on departure time are examined.