Department of Infrastructure Engineering, University of Melbourne, 3010 Parkville, Victoria, Australia.
Sensors (Basel). 2020 Sep 13;20(18):5226. doi: 10.3390/s20185226.
All established models in transportation engineering that estimate the numbers of trips between origins and destinations from vehicle counts use some form of a priori knowledge of the traffic. This paper, in contrast, presents a new origin-destination flow estimation model that uses only vehicle counts observed by traffic count sensors; it requires neither historical origin-destination trip data for the estimation nor any assumed distribution of flow. This approach utilises a method of statistical origin-destination flow estimation in computer networks, and transfers the principles to the domain of road traffic by applying transport-geographic constraints in order to keep traffic embedded in physical space. Being purely stochastic, our model overcomes the conceptual weaknesses of the existing models, and additionally estimates travel times of individual vehicles. The model has been implemented in a real-world road network in the city of Melbourne, Australia. The model was validated with simulated data and real-world observations from two different data sources. The validation results show that all the origin-destination flows were estimated with a good accuracy score using link count data only. Additionally, the estimated travel times by the model were close approximations to the observed travel times in the real world.
所有在交通工程中用于从车辆计数估计起点和终点之间出行次数的既定模型都使用某种形式的事先了解交通情况。相比之下，本文提出了一种新的起点-终点流量估计模型，该模型仅使用交通计数传感器观察到的车辆计数；它既不需要用于估计的历史起点-终点出行数据，也不需要任何假设的流量分布。该方法利用计算机网络中统计起点-终点流量估计的方法，并通过应用交通地理约束将原理转移到道路交通领域，以保持交通嵌入物理空间。由于是纯粹的随机模型，因此我们的模型克服了现有模型的概念性弱点，并且还可以估计单个车辆的旅行时间。该模型已在澳大利亚墨尔本的真实道路网络中实施。该模型使用模拟数据和来自两个不同数据源的实际观测结果进行了验证。验证结果表明，仅使用链路计数数据就能很好地准确估计所有起点-终点流量。此外，模型估计的旅行时间与实际世界中的实际旅行时间非常接近。
