Citation

Moshahedi N, Kattan L. Transp. Res. C Emerg. Technol. 2022; 145: e103911.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.trc.2022.103911

PMID

unavailable

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.

Language: en

Keywords

Connected and autonomous vehicles; Dynamic network loading; Macroscopic fundamental diagram; Variational theory