arXiv 1906.047392019-06-11eess.SPcs.AIcs.LGstat.ML

动态交通分配中的出行表估计与预测

Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications

DATA61|CSIRO（联邦科学与工业研究组织数据61所）；University of Technology, Sydney（悉尼科技大学）

Sajjad Shafiei, Adriana-Simona Mihaita, Chen Cai

备注：: 6 pages, 6 figures, preprint at the 26th ITS World Congress 21-25 Oct 2019

摘要：

研究的重点是估计和预测时变的起点到目的地（OD）的动态交通分配（DTA）模型的行程表。一个双层优化问题，制定和解决预先存在的需求矩阵和历史交通流量计数估计OD流量。然后将估计的需求作为时间序列OD需求预测模型的输入，以支持DTA模型进行短期交通状况预测。结果表明，所提出的OD需求估计方法具有很高的能力，以减少DTA模型的误差，通过迭代求解算法。此外，OD需求预测方法的适用性进行了调查，在澳大利亚悉尼的一个主要走廊的事件分析应用。

英文摘要：

The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model. A bi-level optimisation problem is formulated and solved to estimate OD flows from pre-existent demand matrix and historical traffic flow counts. The estimated demand is then considered as an input for a time series OD demand prediction model to support the DTA model for short-term traffic condition forecasting. Results show a high capability of the proposed OD demand estimation method to reduce the DTA model error through an iterative solution algorithm. Moreover, the applicability of the OD demand prediction approach is investigated for an incident analysis application for a major corridor in Sydney, Australia.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。