Estimating fundamental diagram for multi-modal signalized urban links with limited probe data

Being one of the most classic concepts in the traffic flow theory, the fundamental diagram (FD) describes the relationship between average flow and average density of link-level traffic flow dynamics. Inductive loop detectors or closed-circuit television are commonly used for FD estimations and they are known to have cost-effective and accuracy issues. Thanks to the GPS-enabled smartphones and GPS-equipped probe vehicles, high temporal and spatial resolution traffic data are available which enable traffic condition inference over time and space continuously. Several existing studies have explored FD estimation algorithms on freeways where flow is generally uninterrupted and uni-modal, based on GPS trajectory data. These developments motivate this study, where the objective is to extend the application to multi-modal and interrupted environment, i.e., urban signalized areas. In this paper, an estimation method is developed to capture the FD of multi-modal traffic streams on signalized urban links. The proposed algorithm is empirically tested using real-world GPS datasets collected on a signalized arterial road in Shenzhen City. Promising results show that the proposed algorithm is capable to estimate the FD under such condition. Furthermore, impacts of multi-modal traffic and signal operations on the FD estimation are analyzed and discussed. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

The fundamental diagram (FD) is a crucial concept in traffic flow theory, which describes the relationship between average flow and average density. Traditional FD estimation methods using loop detectors or CCTV have cost and accuracy issues, but GPS-enabled smartphones and vehicles provide high temporal and spatial resolution traffic data for continuous traffic condition inference. This paper proposes an estimation method to capture the FD of multi-modal traffic streams in urban signalized areas. Empirical tests using real-world GPS datasets show promising results, and the impacts of multi-modal traffic and signal operations on FD estimations are analyzed and discussed.