期刊
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
卷 11, 期 1, 页码 376-407出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/21680566.2022.2080128
关键词
O-D matrix estimation; data-driven assignment; empirical assignment matrix
This paper proposes a Data-Driven Network Assignment (DDNA) mechanism for estimating time-dependent Origin-Destination (OD) matrices using Floating-Car Data (FCD). The results of synthetic-data experiments indicate that the computationally expensive Dynamic Traffic Assignment (DTA) models may not be necessary for solving the OD matrix estimation problem if FCD is available.
Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem.
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