A macroscopic dynamic network loading model using variational theory in a connected and autonomous vehicle environment. (December 2022)

• Record Type: Journal Article
• Authors: Moshahedi, Nadia; Kattan, Lina
• Is Part Of: Transportation research. Volume 145(2022)
• Publication Date: 2022-12
• Subjects: Dynamic network loading -- Macroscopic fundamental diagram -- Variational theory -- Connected and autonomous vehicles; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2022.103911
• Languages: English
• ISSNs: 0968-090X

Abstract

Abstract: Recent studies on macroscopic fundamental diagram (MFD) have shifted towards enhancing MFD dynamics in terms of efficiency and realism. In this paper, a multi-reservoir dynamic network loading (MRDNL) model for a large-scale urban road network is developed. The proposed framework consists of a hyper-link and a hyper-node model. The hyper-link model is constructed using variational theory (VT) by introducing a set of constraints that construct an upper bound for upstream and downstream cumulative accumulations. Using VT, LWR is incorporated into MFD dynamics to enhance traffic propagation at reservoirs. The hyper-link model captures important traffic phenomena, such as kinematic waves, queueing, and congestion, while the hyper-node model functions as a medium for transferring flow to other parts of the multi-reservoir urban network. A numerical scheme is advanced for solving the proposed MRDNL model and its performance is evaluated using a hypothetical traffic network. Compared to previous MFD-based dynamic network loading models, our approach is more computationally efficient since it does not require discretization and offers more realistic solutions by considering multiple kinematic waves and including the dynamics of urban traffic signals. This study, further, incorporated connected and autonomous vehicles (CAVs) into MFD dynamics and evaluated the network-wide effect of introduction of CAVs in urban traffic networks. For a network with 100% CAV market penetration rate, an approximate enhancement of 30% in total network outflow was found. Highlights: A multi-reservoir DNL model (MRDNL) for a large-scale urban network is developed. Flow propagation in a multi-reservoir network is modelled. MRDNL offers a more realistic solution by capturing multiple kinematic waves. MRDNL incorporates connected and autonomous vehicle dynamics at networkwide level. Sensitivity analysis of the CAV penetration rate and information level are conducted.