Lu Bai , Wai Wong , Pengpeng Xu , Pan Liu , Andy H.F. Chow , William H.K. Lam , Wei Ma , Yu Han , S.C. Wong
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引用次数: 0
摘要
使用时间分辨率不同的多源数据估算交通流模型(如速度-密度关系),是现实世界中普遍遇到的难题。解决分辨率不兼容问题的直观方法通常是对高分辨率(HR)数据进行平均处理,使其与低分辨率(LR)数据同步。本文表明,在平均过程中忽略低分辨率区间内高分辨率数据的可变性,会导致系统数据点失真,从而造成模型估计偏差。根据平均数据估计出的模型的平均绝对偏差会随着 LR 间隔内心率数据变异性的丢失而增加。随后,研究证明,对于任何给定的完整平均数据数据集,一定存在一个最优数据集,该数据集能最大限度地减少平均化过程引入的模型估计平均绝对偏差。本文提出了一种确定实用最优数据集的新程序。为了测试所提出的方法,我们收集了来自中国香港和南京四个地点的实际人力资源数据,以模拟多分辨率数据的情况。结果表明,与从完整的平均数据集估算出的模型相比,所提出的方法能显著减少从确定的实用最佳数据集估算出的模型的平均绝对偏差。
Fusion of multi-resolution data for estimating speed-density relationships
Estimating traffic flow models, such as speed-density relationships, using data from multiple sources with different temporal resolutions is a prevalent challenge encountered in real-world scenarios. The resolution incompatibility is often intuitively addressed by averaging the high-resolution (HR) data to synchronize with the low-resolution (LR) data. This paper shows that ignoring the variability of HR data within the LR interval during the averaging process could lead to systematic data point distortions, resulting in biased model estimations. The average absolute biases of models estimated from the average data increase with the lost variability of HR data within the LR intervals. Subsequently, it proves that for any given complete average data dataset, there must exist an optimal dataset that minimizes the average absolute bias in model estimations introduced by the averaging process. A novel procedure for determining the practical optimal dataset is proposed. To test the proposed method, real-world HR data from four sites in Hong Kong and Nanjing, China were collected to mimic situations with multi-resolution data. Results demonstrated that the proposed method can significantly reduce the average absolute biases of models estimated from the determined practical optimal dataset, as compared to models estimated from the complete average dataset.
期刊介绍:
Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.